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Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration

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UPMC - Sorbonne Universities

The document presents a model called CRAQ designed for recommending answerers on Twitter questions through social collaboration. It discusses the motivation for using social media for question asking, the limitations of current platforms, and the need for a collaborative approach to enhance information retrieval. The CRAQ model emphasizes forming a group of users with complementary skills to provide cohesive and relevant answers, supported by experimental evaluations.

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1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration Laure Soulier Pierre and Marie Curie University LIP6, Paris - France Lynda Tamine Paul Sabatier University IRIT, Toulouse - France Gia-Hung Nguyen Paul Sabatier University IRIT, Toulouse - France October 25, 2016 1 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations Social media-based information access Collaboration and social media-based informationa access 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion 2 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Activity on social platforms • Social networks: communication tool for the general public 3 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? 4 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] 4 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] • Limitations of social platforms 4 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] • Limitations of social platforms Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. 4 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] • Limitations of social platforms Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. Design implications • Enhancement of social awareness (creating social ties to active/relevant users) • Recommendation of collaborators (asking questions to crowd instead of followers) 4 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS COLLABORATION AND SOCIAL MEDIA-BASED INFORMATION ACCESS: TWO SIDE IN THE SAME COIN? • Social media-based information access Seeking, answering, sharing, bookmarking, and spreading information Improving the search outcomes through social interactions • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team 5 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS COLLABORATION AND SOCIAL MEDIA-BASED INFORMATION ACCESS: TWO SIDE IN THE SAME COIN? • Social media-based information access Seeking, answering, sharing, bookmarking, and spreading information Improving the search outcomes through social interactions • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team • Social media-based collaboration Leveraging from the ”wisdom of the crowd” Implicit or explicit intents (sharing, questioning, and/or answering) Tasks: social question-answering, social search, real-time search 5 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS COLLABORATION AND SOCIAL MEDIA-BASED INFORMATION ACCESS: TWO SIDE IN THE SAME COIN? • Social media-based information access Seeking, answering, sharing, bookmarking, and spreading information Improving the search outcomes through social interactions • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team • Social media-based collaboration Leveraging from the ”wisdom of the crowd” Implicit or explicit intents (sharing, questioning, and/or answering) Tasks: social question-answering, social search, real-time search Our contribution • Identifying a group of socially authoritative users with complementary skills to overpass the local social network • Gathering diverse pieces of information → Recommending a group of collaborators 5 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work Pioneering work Comparison of previous work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion 6 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion RELATED WORK PIONEERING WORK: AARDVARK [HOROWITZ AND KAMVAR, 2010] Aardvark [Horowitz and Kamvar, 2010] • The village paradigm: towards a social dissemination of knowledge Information is passed from person to person Finding the right person rather than the right document 7 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion RELATED WORK PIONEERING WORK: SEARCHBUDDIES [HECHT ET AL., 2012] SearchBuddies [Hecht et al., 2012] • A crowd-powered socially embedded search engine • Leveraging users’ personal network to reach the right people/information • Soshul Butterflie: Recommending people • Investigaetore: Recommending urls 8 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion RELATED WORK COMPARISON OF PREVIOUS APPROACHES Previous work Expertise/interest Responsiveness Socialactivity U sers’connectedness C om patibility O ptim ization ofthe outcom e C om plem entarity of users’skills Reco.users Expert finding [Balog et al., 2012] Authoritative users/influencers [Pal and Counts, 2011] Aardvark [Horowitz and Kamvar, 2010] SearchBuddies [Hecht et al., 2012] Mentionning users/spreaders [Wang et al., 2013, Gong et al., 2015] Reco.groupofusers CrowdStar [Nushi et al., 2015] Question routing for collab. Q&A[Chang and Pal, 2013] Recommended targeted stranger [Mahmud et al., 2013] Crowdworker [Ranganath et al., 2015] Our work 9 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work 3. The CRAQ Model Overview Learning the pairwise collaboration likelihood Building the collaborative group of users 4. Experimental Evaluation 5. Conclusion 10 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM OVERVIEW • Indentifying a group of complementary answerers who could provide the questioner with a cohesive and relevant answer. • Gathering diverse pieces of information posted by users • Maximization of the group entropy 11 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE A: LEARNING THE PAIRWISE COLLABORATION LIKELIHOOD Objective Estimating the potential of collaboration between a pair of users • Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014] 12 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE A: LEARNING THE PAIRWISE COLLABORATION LIKELIHOOD Objective Estimating the potential of collaboration between a pair of users • Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014] 12 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE A: LEARNING THE PAIRWISE COLLABORATION LIKELIHOOD Objective Estimating the potential of collaboration between a pair of users • Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014] 12 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE B: BUILDING THE COLLABORATIVE GROUP OF USERS Objective Building the smallest group of collaborators maximizing the cohesiveness and relevance of the collaborative response • Identifying candidate collaborators through a temporal ranking model [Berberich and Bedathur, 2013] 13 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE B: BUILDING THE COLLABORATIVE GROUP OF USERS Objective Building the smallest group of collaborators maximizing the cohesiveness and relevance of the collaborative response • Extracting the collaborator group Maximizing entropy equivalent to minimizing the information gain [Quinlan, 1986] IG(g, uk) = [H(g) ↓ H(g) ∝ − uj∈g P(uj|q) · log(P(uj|q)) − H(g|uk) ↓ H(g|uk) = p(uk) · [− uj∈g uj=uk P(uj|uk) · log(P(uj|uk))] ] (1) Recursive decrementation of candidate collaborators through the information gain metric t ∗ = arg max t∈[0,...,|U|−1] ∂2IGr(gt,u) ∂u2 |u=ut (2) Given u t = argminu j ∈gt IGr(gt , uj ) And g t+1 = gt ut 14 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation Evaluation Protocol Results 5. Conclusion 15 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL Evaluation objectives • RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • RQ2: Are the recommended group-based answers relevant? • RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Datasets 1 Hurricane #Sandy (October 2012) 2 #Ebola virus epidemic (2013-2014) Collection Sandy Ebola Tweets 2,119,854 2,872,890 Microbloggers 1,258,473 750,829 Retweets 963,631 1,157,826 Mentions 1,473,498 1,826,059 Reply 63,596 69,773 URLs 596,393 1,309,919 Pictures 107,263 310,581 16 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Question identification [Jeong et al., 2013] Filtering tweets ending with a question mark Excluding mention tweets and tweets including URLs Filtering tweets with question-oriented hashtags [Jeffrey M. Rzeszotarski, 2014] (e.g., #help, #askquestion, ...) Excluding rhetorical questions (Crowdflower) Sandy 41 questions Would love to #help to clear up the mess #Sandy made. Any way people can help? Voluntery groups? Ebola 21 questions How do you get infected by this Ebola virus though?? #Twoogle 17 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Question identification [Jeong et al., 2013] Filtering tweets ending with a question mark Excluding mention tweets and tweets including URLs Filtering tweets with question-oriented hashtags [Jeffrey M. Rzeszotarski, 2014] (e.g., #help, #askquestion, ...) Excluding rhetorical questions (Crowdflower) Sandy 41 questions Would love to #help to clear up the mess #Sandy made. Any way people can help? Voluntery groups? Ebola 21 questions How do you get infected by this Ebola virus though?? #Twoogle • Collaboration likelihood features Name Description Authority Importance Number of followers Number of followings Number of favorites Engagement Number of tweets Activity Number of topically-related tweets within the In-degree in the topic topic Out-degree in the topic Complementarity Topic Jansen-Shanon distance between topical- based representation of users’ interests obtained through the LDA algorithm Multimedia Number of tweets with video Number of tweets with images Number of tweets with links Number of tweets with hashtags Number of tweets with only text Opinion Number of tweets with positive opinion polarity Number of tweets with neutral opinion Authority-based features (trust and expertise of each user) Xjj = log( µ(Xj, Xj ) σ(Xj, Xj ) ) (3) Complementarity-based features (complementairty of collaborators) Xjj = |Xj − Xj | Xj + Xj (4) 17 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Baselines MMR: diversity-based ranking model of tweets [Carbonell and Goldstein, 1998] (tweet level) U: best user of the temporal ranking model [Berberich and Bedathur, 2013] (individual) CRAQ-TR: CRAQ w/o group entropy maximization SM: community detection algorithm based on the graph structure [Cao et al., 2015] STM: Topical Sensitive PageRank applied on users [Haveliwala, 2002] • Evaluation workflow Question + Tweets of users Evaluating tweets and building an answer Assessing the relevance of users’ tweets of built answers Ground truth 18 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Baselines MMR: diversity-based ranking model of tweets [Carbonell and Goldstein, 1998] (tweet level) U: best user of the temporal ranking model [Berberich and Bedathur, 2013] (individual) CRAQ-TR: CRAQ w/o group entropy maximization SM: community detection algorithm based on the graph structure [Cao et al., 2015] STM: Topical Sensitive PageRank applied on users [Haveliwala, 2002] • Evaluation workflow Question + Tweets of users Evaluating tweets and building an answer Assessing the relevance of users’ tweets of built answers Ground truth Question Top ranked tweets of recommended group members Answer built by the crowd Would love to #help to clear up the mess #Sandy made. Any way people can help? Voluntery groups? - My prayers go out to those people out there that have been affected by the storm. #Sandy - Makes me want to volunteer myself and help the Red Cross and rescue groups.#Sandy - Rescue groups are organized and dispatched to help animals in Sandy’s aftermath. You can help by donating. #SandyPets - ASPCA, HSUS, American Humane are among groups on the ground helping animals in Sandy’s aftermath. Help them with a donation. #SandyPets #wlf Rescue groups are organized and dis- patched ASPCA, HSUS, American Hu- mane, Donate to @RedCross, @Hu- maneSociety, @ASPCA. 18 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Baselines MMR: diversity-based ranking model of tweets [Carbonell and Goldstein, 1998] (tweet level) U: best user of the temporal ranking model [Berberich and Bedathur, 2013] (individual) CRAQ-TR: CRAQ w/o group entropy maximization SM: community detection algorithm based on the graph structure [Cao et al., 2015] STM: Topical Sensitive PageRank applied on users [Haveliwala, 2002] • Evaluation workflow Question + Tweets of users Evaluating tweets and building an answer Assessing the relevance of users’ tweets of built answers Ground truth 18 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. 0 1 2 3 10 20 30 40 50 Sandy 0 1 2 3 0 10 20 30 40 50 60 70 Ebola MMR U CRAQ-TR SM STM CRAQ • Lowest rate for the Not related category (0) 19 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. 0 1 2 3 10 20 30 40 50 Sandy 0 1 2 3 0 10 20 30 40 50 60 70 Ebola MMR U CRAQ-TR SM STM CRAQ • Lowest rate for the Not related category (0) • Highest proportion of 2+3 Related and helpful 19 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. 0 1 2 3 10 20 30 40 50 Sandy 0 1 2 3 0 10 20 30 40 50 60 70 Ebola MMR U CRAQ-TR SM STM CRAQ • Lowest rate for the Not related category (0) • Highest proportion of 2+3 Related and helpful • Complementarity of tweets is not satisfying w.r.t. baselines Negative regression estimate of complementarity-based features in the collaboration likelihood model - phase A 19 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. • Testing whether those provided tweets allow building a cohesive answer. Sandy Ebola 10 15 20 25 30 35 Avg Percentage of selected tweets Sandy Ebola 10 20 30 40 50 60 70 80 Number of built answers MMR U CRAQ-TR SM STM CRAQ • U: highest rate of selected tweets / lowest number of built answers 20 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. • Testing whether those provided tweets allow building a cohesive answer. Sandy Ebola 10 15 20 25 30 35 Avg Percentage of selected tweets Sandy Ebola 10 20 30 40 50 60 70 80 Number of built answers MMR U CRAQ-TR SM STM CRAQ • U: highest rate of selected tweets / lowest number of built answers • CRAQ: Lack of tweet complementarity does not impact the ability of the recommended group to answer the query 20 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ2: Are the recommended group-based answers relevant? • Testing the relevance of the built answers MMR U CRAQ-TR SM STM CRAQ Sandy ba 43 29 75 74 67 77 1 11: 25.58% 9: 31.03% 23: 30.67% 24: 32.43% 21: 31.34% 17: 22.08% 2 20: 46.51% 14: 48.28% 33: 44.00% 34: 45.95% 24: 35.82% 39: 50.65% 3 12: 27.91% 6: 20.69% 19: 25.33% 16: 21.62% 22: 32.84% 21: 27.27% 2+3 32: 74.42% 20: 68.97% 52: 69.33% 50: 67.57% 46: 68.66% 60: 77.92% Ebola ba 22 11 39 30 37 41 1 4: 21.05% 3: 27.27% 15: 38.46% 8: 26.67% 15: 40.54% 10: 24.39% 2 6: 31.58% 4: 36.36% 18: 46.15% 15: 50% 16: 43.24% 22: 53.66% 3 9: 47.37% 4: 36.36% 6: 15.38% 7: 23.33% 6: 16.22% 9: 21.95% 2+3 15: 78.95% 8: 72.72% 24: 1.53% 22: 73.33% 22:59.46% 31: 75.61% • CRAQ enables to build a higher number of answers, among them a higher proportion of Partly relevant and Relevant answers U: reinforces our intuition that a single user might have an insufficient knowledge (even if related) to solve a tweeted question. 21 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ2: Are the recommended group-based answers relevant? • Testing the relevance of the built answers MMR U CRAQ-TR SM STM CRAQ Sandy ba 43 29 75 74 67 77 1 11: 25.58% 9: 31.03% 23: 30.67% 24: 32.43% 21: 31.34% 17: 22.08% 2 20: 46.51% 14: 48.28% 33: 44.00% 34: 45.95% 24: 35.82% 39: 50.65% 3 12: 27.91% 6: 20.69% 19: 25.33% 16: 21.62% 22: 32.84% 21: 27.27% 2+3 32: 74.42% 20: 68.97% 52: 69.33% 50: 67.57% 46: 68.66% 60: 77.92% Ebola ba 22 11 39 30 37 41 1 4: 21.05% 3: 27.27% 15: 38.46% 8: 26.67% 15: 40.54% 10: 24.39% 2 6: 31.58% 4: 36.36% 18: 46.15% 15: 50% 16: 43.24% 22: 53.66% 3 9: 47.37% 4: 36.36% 6: 15.38% 7: 23.33% 6: 16.22% 9: 21.95% 2+3 15: 78.95% 8: 72.72% 24: 1.53% 22: 73.33% 22:59.46% 31: 75.61% • CRAQ enables to build a higher number of answers, among them a higher proportion of Partly relevant and Relevant answers U: reinforces our intuition that a single user might have an insufficient knowledge (even if related) to solve a tweeted question. MMR: gives rise to the benefit of building answers from the users perspective rather than the tweets regardless of their context. 21 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ2: Are the recommended group-based answers relevant? • Testing the relevance of the built answers MMR U CRAQ-TR SM STM CRAQ Sandy ba 43 29 75 74 67 77 1 11: 25.58% 9: 31.03% 23: 30.67% 24: 32.43% 21: 31.34% 17: 22.08% 2 20: 46.51% 14: 48.28% 33: 44.00% 34: 45.95% 24: 35.82% 39: 50.65% 3 12: 27.91% 6: 20.69% 19: 25.33% 16: 21.62% 22: 32.84% 21: 27.27% 2+3 32: 74.42% 20: 68.97% 52: 69.33% 50: 67.57% 46: 68.66% 60: 77.92% Ebola ba 22 11 39 30 37 41 1 4: 21.05% 3: 27.27% 15: 38.46% 8: 26.67% 15: 40.54% 10: 24.39% 2 6: 31.58% 4: 36.36% 18: 46.15% 15: 50% 16: 43.24% 22: 53.66% 3 9: 47.37% 4: 36.36% 6: 15.38% 7: 23.33% 6: 16.22% 9: 21.95% 2+3 15: 78.95% 8: 72.72% 24: 1.53% 22: 73.33% 22:59.46% 31: 75.61% • CRAQ enables to build a higher number of answers, among them a higher proportion of Partly relevant and Relevant answers U: reinforces our intuition that a single user might have an insufficient knowledge (even if related) to solve a tweeted question. MMR: gives rise to the benefit of building answers from the users perspective rather than the tweets regardless of their context. CRAQ-TR (best baseline): building a group by gathering individual users identified as relevant through their skills (tweet topical similarity with the question) is not always appropriate. 21 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context 22 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group 22 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group • CRAQ-TR: no significant differences in effectiveness / lower ratio of relevant answers. Benefit of the group entropy maximization based on the collaboration likelihood. 22 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group • CRAQ-TR: no significant differences in effectiveness / lower ratio of relevant answers. Benefit of the group entropy maximization based on the collaboration likelihood. • SM: benefit of overpassing strong ties (users’ local network) to select relevant strangers 22 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group • CRAQ-TR: no significant differences in effectiveness / lower ratio of relevant answers. Benefit of the group entropy maximization based on the collaboration likelihood. • SM: benefit of overpassing strong ties (users’ local network) to select relevant strangers • STM: topically relevant tweets issued from the most socially authoritative are not obviously relevant to answer the tweeted question. 22 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion 23 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONCLUSION AND PERSPECTIVES Discussion • Novel method for answering questions on social networks: recommending a group of socially active and complementary collaborators. • Relevant factors: Information gain provided by a user to the group Complementarity and topical relevance of the related tweets Trust and authority of the group members • Method applicable for other social platforms (Facebook, community Q&A sites, ...) 24 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONCLUSION AND PERSPECTIVES Discussion • Novel method for answering questions on social networks: recommending a group of socially active and complementary collaborators. • Relevant factors: Information gain provided by a user to the group Complementarity and topical relevance of the related tweets Trust and authority of the group members • Method applicable for other social platforms (Facebook, community Q&A sites, ...) Future Directions • Limitation of the predictive model of collaboration likelihood relying on basic assumptions of collaborations (mentions, replies, retweets). Deeper analysis of collaboration behavior on social networks to identify collaboration patterns. • Automatic summarization of candidate answers to build a collaborative answer. 24 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THANK YOU! @LaureSoulier @LyndaTamine @ngiahung 25 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES I Balog, K., Fang, Y., de Rijke, M., Serdyukov, P., and Si, L. (2012). Expertise retrieval. Foundations and Trends in Information Retrieval, 6(2-3):127–256. Berberich, K. and Bedathur, S. (2013). Temporal Diversification of Search Results. In SIGIR #TAIA workshop. ACM. Cao, C., Caverlee, J., Lee, K., Ge, H., and Chung, J. (2015). Organic or organized?: Exploring URL sharing behavior. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pages 513–522. Carbonell, J. and Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the Annual International SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’98, pages 335–336. ACM. Chang, S. and Pal, A. (2013). Routing questions for collaborative answering in community question answering. In ASONAM ’13, pages 494–501. ACM. Ehrlich, K. and Shami, N. S. (2010). Microblogging inside and outside the workplace. In Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010. Evans, B. M. and Chi, E. H. (2010). An elaborated model of social search. Information Processing & Management (IP&M), 46(6):656–678. 26 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES II Fuchs, C. and Groh, G. (2015). Appropriateness of search engines, social networks, and directly approaching friends to satisfy information needs. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, pages 1248–1253. Gong, Y., Zhang, Q., Sun, X., and Huang, X. (2015). Who will you ”@”? pages 533–542. ACM. Harper, F. M., Raban, D. R., Rafaeli, S., and Konstan, J. A. (2008). Predictors of answer quality in online q&a sites. In Proceedings of the 2008 Conference on Human Factors in Computing Systems, CHI 2008, 2008, Florence, Italy, April 5-10, 2008, pages 865–874. Haveliwala, T. H. (2002). Topic-sensitive PageRank. In Proceedings of the International Conference on World Wide Web, WWW ’02, pages 517–526. ACM. Hecht, B., Teevan, J., Morris, M. R., and Liebling, D. J. (2012). Searchbuddies: Bringing search engines into the conversation. In WSDM ’14. Honey, C. and Herring, S. (2009). Beyond Microblogging: Conversation and Collaboration via Twitter. In HICSS, pages 1–10. Horowitz, D. and Kamvar, S. D. (2010). The Anatomy of a Large-scale Social Search Engine. In WWW ’10, pages 431–440. ACM. 27 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES III Jeffrey M. Rzeszotarski, M. R. M. (2014). Estimating the social costs of friendsourcing. In Proceedings of CHI 2014. ACM. Jeong, J.-W., Morris, M. R., Teevan, J., and Liebling, D. (2013). A crowd-powered socially embedded search engine. In ICWSM ’13. AAAI. Liu, Z. and Jansen, B. J. (2013). Factors influencing the response rate in social question and answering behavior. In Computer Supported Cooperative Work, CSCW 2013, pages 1263–1274. Mahmud, J., Zhou, M. X., Megiddo, N., Nichols, J., and Drews, C. (2013). Recommending targeted strangers from whom to solicit information on social media. In IUI ’13, pages 37–48. ACM. McNally, K., O’Mahony, M. P., and Smyth, B. (2013). A model of collaboration-based reputation for the social web. In ICWSM. Morris, M. R. (2013). Collaborative Search Revisited. In Proceedings of the Conference on Computer Supported Cooperative Work, CSCW ’13, pages 1181–1192. ACM. Morris, M. R., Teevan, J., and Panovich, K. (2010). What do people ask their social networks, and why?: a survey study of status message q&a behavior. In Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, pages 1739–1748. 28 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES IV Nushi, B., Alonso, O., Hentschel, M., and Kandylas, V. (2015). Crowdstar: A social task routing framework for online communities. In ICWE ’15, pages 219–230. Pal, A. and Counts, S. (2011). Identifying topical authorities in microblogs. In WSDM ’11, pages 45–54. ACM. Paul, S. A., Hong, L., and Chi, E. H. (2011). Is twitter a good place for asking questions? A characterization study. In Proceedings of the Fifth International Conference on Weblogs and Social Media. Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1(1):81–106. Ranganath, S., Wang, S., Hu, X., Tang, J., and Liu, H. (2015). Finding time-critical responses for information seeking in social media. In 2015 IEEE International Conference on Data Mining, ICDM 2015, pages 961–966. Rzeszotarski, J. M., Spiro, E. S., Matias, J. N., Monroy-Hern´andez, A., and Morris, M. R. (2014). Is anyone out there?: unpacking q&a hashtags on twitter. In CHI Conference on Human Factors in Computing Systems, CHI’14, pages 2755–2758. Sonnenwald, D. H., Maglaughlin, K. L., and Whitton, M. C. (2004). Designing to support situation awareness across distances: an example from a scientific collaboratory. Information Processing & Management (IP&M), 40(6):989–1011. 29 / 30
1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES V Soulier, L., Shah, C., and Tamine, L. (2014). User-driven System-mediated Collaborative Information Retrieval. In SIGIR ’14, pages 485–494. ACM. Tamine, L., Soulier, L., Jabeur, L. B., Amblard, F., Hanachi, C., Hubert, G., and Roth, C. (2016). Social media-based collaborative information access: Analysis of online crisis-related twitter conversations. In HT ’16. Teevan, J., Ramage, D., and Morris, M. R. (2011). #twittersearch: a comparison of microblog search and web search. In Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, pages 35–44. Wang, B., Wang, C., Bu, J., Chen, C., Zhang, W. V., Cai, D., and He, X. (2013). Whom to mention: Expand the diffusion of tweets by @ recommendation on micro-blogging systems. In Proceedings of the 22Nd International Conference on World Wide Web, WWW ’13, pages 1331–1340. ACM. 30 / 30

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Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration

  • 1. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration Laure Soulier Pierre and Marie Curie University LIP6, Paris - France Lynda Tamine Paul Sabatier University IRIT, Toulouse - France Gia-Hung Nguyen Paul Sabatier University IRIT, Toulouse - France October 25, 2016 1 / 30
  • 2. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations Social media-based information access Collaboration and social media-based informationa access 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion 2 / 30
  • 3. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Activity on social platforms • Social networks: communication tool for the general public 3 / 30
  • 4. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? 4 / 30
  • 5. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] 4 / 30
  • 6. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] • Limitations of social platforms 4 / 30
  • 7. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] • Limitations of social platforms Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. 4 / 30
  • 8. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS SOCIAL MEDIA-BASED INFORMATION ACCESS • Why choosing social platforms for asking questions? Large audience and wide range of topics [Harper et al., 2008, Jeong et al., 2013, Tamine et al., 2016] Specific audience, expertise → trust, personalisation, and contextualisation [Morris et al., 2010] Friendsourcing through people addressing (”@”, forward) [Liu and Jansen, 2013, Teevan et al., 2011, Fuchs and Groh, 2015] Communication, exchange, sensemaking [Morris, 2013, Evans and Chi, 2010, Tamine et al., 2016] • Limitations of social platforms Majority of questions without response [Jeong et al., 2013, Paul et al., 2011] Answers mostly provided by members of the immediate follower network [Morris et al., 2010, Rzeszotarski et al., 2014] Social and cognitive cost of friendsourcing (e.g., spent time and deployed effort) [Horowitz and Kamvar, 2010, Morris, 2013]. Design implications • Enhancement of social awareness (creating social ties to active/relevant users) • Recommendation of collaborators (asking questions to crowd instead of followers) 4 / 30
  • 9. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS COLLABORATION AND SOCIAL MEDIA-BASED INFORMATION ACCESS: TWO SIDE IN THE SAME COIN? • Social media-based information access Seeking, answering, sharing, bookmarking, and spreading information Improving the search outcomes through social interactions • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team 5 / 30
  • 10. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS COLLABORATION AND SOCIAL MEDIA-BASED INFORMATION ACCESS: TWO SIDE IN THE SAME COIN? • Social media-based information access Seeking, answering, sharing, bookmarking, and spreading information Improving the search outcomes through social interactions • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team • Social media-based collaboration Leveraging from the ”wisdom of the crowd” Implicit or explicit intents (sharing, questioning, and/or answering) Tasks: social question-answering, social search, real-time search 5 / 30
  • 11. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONTEXT AND MOTIVATIONS COLLABORATION AND SOCIAL MEDIA-BASED INFORMATION ACCESS: TWO SIDE IN THE SAME COIN? • Social media-based information access Seeking, answering, sharing, bookmarking, and spreading information Improving the search outcomes through social interactions • Collaboration Identifying and solving a shared complex problem Creating and sharing knowledge within a work team • Social media-based collaboration Leveraging from the ”wisdom of the crowd” Implicit or explicit intents (sharing, questioning, and/or answering) Tasks: social question-answering, social search, real-time search Our contribution • Identifying a group of socially authoritative users with complementary skills to overpass the local social network • Gathering diverse pieces of information → Recommending a group of collaborators 5 / 30
  • 12. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work Pioneering work Comparison of previous work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion 6 / 30
  • 13. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion RELATED WORK PIONEERING WORK: AARDVARK [HOROWITZ AND KAMVAR, 2010] Aardvark [Horowitz and Kamvar, 2010] • The village paradigm: towards a social dissemination of knowledge Information is passed from person to person Finding the right person rather than the right document 7 / 30
  • 14. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion RELATED WORK PIONEERING WORK: SEARCHBUDDIES [HECHT ET AL., 2012] SearchBuddies [Hecht et al., 2012] • A crowd-powered socially embedded search engine • Leveraging users’ personal network to reach the right people/information • Soshul Butterflie: Recommending people • Investigaetore: Recommending urls 8 / 30
  • 15. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion RELATED WORK COMPARISON OF PREVIOUS APPROACHES Previous work Expertise/interest Responsiveness Socialactivity U sers’connectedness C om patibility O ptim ization ofthe outcom e C om plem entarity of users’skills Reco.users Expert finding [Balog et al., 2012] Authoritative users/influencers [Pal and Counts, 2011] Aardvark [Horowitz and Kamvar, 2010] SearchBuddies [Hecht et al., 2012] Mentionning users/spreaders [Wang et al., 2013, Gong et al., 2015] Reco.groupofusers CrowdStar [Nushi et al., 2015] Question routing for collab. Q&A[Chang and Pal, 2013] Recommended targeted stranger [Mahmud et al., 2013] Crowdworker [Ranganath et al., 2015] Our work 9 / 30
  • 16. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work 3. The CRAQ Model Overview Learning the pairwise collaboration likelihood Building the collaborative group of users 4. Experimental Evaluation 5. Conclusion 10 / 30
  • 17. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM OVERVIEW • Indentifying a group of complementary answerers who could provide the questioner with a cohesive and relevant answer. • Gathering diverse pieces of information posted by users • Maximization of the group entropy 11 / 30
  • 18. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE A: LEARNING THE PAIRWISE COLLABORATION LIKELIHOOD Objective Estimating the potential of collaboration between a pair of users • Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014] 12 / 30
  • 19. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE A: LEARNING THE PAIRWISE COLLABORATION LIKELIHOOD Objective Estimating the potential of collaboration between a pair of users • Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014] 12 / 30
  • 20. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE A: LEARNING THE PAIRWISE COLLABORATION LIKELIHOOD Objective Estimating the potential of collaboration between a pair of users • Hypotheses: On Twitter, collaboration between users is noted by the @ symbol [Ehrlich and Shami, 2010, Honey and Herring, 2009] Trust and authority enable to improve the effectiveness of the collaboration [McNally et al., 2013] Collaboration is a structured search process in which users might or might not be complementary [Sonnenwald et al., 2004, Soulier et al., 2014] 12 / 30
  • 21. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE B: BUILDING THE COLLABORATIVE GROUP OF USERS Objective Building the smallest group of collaborators maximizing the cohesiveness and relevance of the collaborative response • Identifying candidate collaborators through a temporal ranking model [Berberich and Bedathur, 2013] 13 / 30
  • 22. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THE CRAQ MODEL: ANSWERING TWITTER QUESTIONS THROUGH A COLLABORATIVE GROUP RECOMMENDATION ALGORITHM STAGE B: BUILDING THE COLLABORATIVE GROUP OF USERS Objective Building the smallest group of collaborators maximizing the cohesiveness and relevance of the collaborative response • Extracting the collaborator group Maximizing entropy equivalent to minimizing the information gain [Quinlan, 1986] IG(g, uk) = [H(g) ↓ H(g) ∝ − uj∈g P(uj|q) · log(P(uj|q)) − H(g|uk) ↓ H(g|uk) = p(uk) · [− uj∈g uj=uk P(uj|uk) · log(P(uj|uk))] ] (1) Recursive decrementation of candidate collaborators through the information gain metric t ∗ = arg max t∈[0,...,|U|−1] ∂2IGr(gt,u) ∂u2 |u=ut (2) Given u t = argminu j ∈gt IGr(gt , uj ) And g t+1 = gt ut 14 / 30
  • 23. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation Evaluation Protocol Results 5. Conclusion 15 / 30
  • 24. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL Evaluation objectives • RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • RQ2: Are the recommended group-based answers relevant? • RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Datasets 1 Hurricane #Sandy (October 2012) 2 #Ebola virus epidemic (2013-2014) Collection Sandy Ebola Tweets 2,119,854 2,872,890 Microbloggers 1,258,473 750,829 Retweets 963,631 1,157,826 Mentions 1,473,498 1,826,059 Reply 63,596 69,773 URLs 596,393 1,309,919 Pictures 107,263 310,581 16 / 30
  • 25. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Question identification [Jeong et al., 2013] Filtering tweets ending with a question mark Excluding mention tweets and tweets including URLs Filtering tweets with question-oriented hashtags [Jeffrey M. Rzeszotarski, 2014] (e.g., #help, #askquestion, ...) Excluding rhetorical questions (Crowdflower) Sandy 41 questions Would love to #help to clear up the mess #Sandy made. Any way people can help? Voluntery groups? Ebola 21 questions How do you get infected by this Ebola virus though?? #Twoogle 17 / 30
  • 26. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Question identification [Jeong et al., 2013] Filtering tweets ending with a question mark Excluding mention tweets and tweets including URLs Filtering tweets with question-oriented hashtags [Jeffrey M. Rzeszotarski, 2014] (e.g., #help, #askquestion, ...) Excluding rhetorical questions (Crowdflower) Sandy 41 questions Would love to #help to clear up the mess #Sandy made. Any way people can help? Voluntery groups? Ebola 21 questions How do you get infected by this Ebola virus though?? #Twoogle • Collaboration likelihood features Name Description Authority Importance Number of followers Number of followings Number of favorites Engagement Number of tweets Activity Number of topically-related tweets within the In-degree in the topic topic Out-degree in the topic Complementarity Topic Jansen-Shanon distance between topical- based representation of users’ interests obtained through the LDA algorithm Multimedia Number of tweets with video Number of tweets with images Number of tweets with links Number of tweets with hashtags Number of tweets with only text Opinion Number of tweets with positive opinion polarity Number of tweets with neutral opinion Authority-based features (trust and expertise of each user) Xjj = log( µ(Xj, Xj ) σ(Xj, Xj ) ) (3) Complementarity-based features (complementairty of collaborators) Xjj = |Xj − Xj | Xj + Xj (4) 17 / 30
  • 27. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Baselines MMR: diversity-based ranking model of tweets [Carbonell and Goldstein, 1998] (tweet level) U: best user of the temporal ranking model [Berberich and Bedathur, 2013] (individual) CRAQ-TR: CRAQ w/o group entropy maximization SM: community detection algorithm based on the graph structure [Cao et al., 2015] STM: Topical Sensitive PageRank applied on users [Haveliwala, 2002] • Evaluation workflow Question + Tweets of users Evaluating tweets and building an answer Assessing the relevance of users’ tweets of built answers Ground truth 18 / 30
  • 28. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Baselines MMR: diversity-based ranking model of tweets [Carbonell and Goldstein, 1998] (tweet level) U: best user of the temporal ranking model [Berberich and Bedathur, 2013] (individual) CRAQ-TR: CRAQ w/o group entropy maximization SM: community detection algorithm based on the graph structure [Cao et al., 2015] STM: Topical Sensitive PageRank applied on users [Haveliwala, 2002] • Evaluation workflow Question + Tweets of users Evaluating tweets and building an answer Assessing the relevance of users’ tweets of built answers Ground truth Question Top ranked tweets of recommended group members Answer built by the crowd Would love to #help to clear up the mess #Sandy made. Any way people can help? Voluntery groups? - My prayers go out to those people out there that have been affected by the storm. #Sandy - Makes me want to volunteer myself and help the Red Cross and rescue groups.#Sandy - Rescue groups are organized and dispatched to help animals in Sandy’s aftermath. You can help by donating. #SandyPets - ASPCA, HSUS, American Humane are among groups on the ground helping animals in Sandy’s aftermath. Help them with a donation. #SandyPets #wlf Rescue groups are organized and dis- patched ASPCA, HSUS, American Hu- mane, Donate to @RedCross, @Hu- maneSociety, @ASPCA. 18 / 30
  • 29. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION EVALUATION PROTOCOL • Baselines MMR: diversity-based ranking model of tweets [Carbonell and Goldstein, 1998] (tweet level) U: best user of the temporal ranking model [Berberich and Bedathur, 2013] (individual) CRAQ-TR: CRAQ w/o group entropy maximization SM: community detection algorithm based on the graph structure [Cao et al., 2015] STM: Topical Sensitive PageRank applied on users [Haveliwala, 2002] • Evaluation workflow Question + Tweets of users Evaluating tweets and building an answer Assessing the relevance of users’ tweets of built answers Ground truth 18 / 30
  • 30. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. 0 1 2 3 10 20 30 40 50 Sandy 0 1 2 3 0 10 20 30 40 50 60 70 Ebola MMR U CRAQ-TR SM STM CRAQ • Lowest rate for the Not related category (0) 19 / 30
  • 31. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. 0 1 2 3 10 20 30 40 50 Sandy 0 1 2 3 0 10 20 30 40 50 60 70 Ebola MMR U CRAQ-TR SM STM CRAQ • Lowest rate for the Not related category (0) • Highest proportion of 2+3 Related and helpful 19 / 30
  • 32. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. 0 1 2 3 10 20 30 40 50 Sandy 0 1 2 3 0 10 20 30 40 50 60 70 Ebola MMR U CRAQ-TR SM STM CRAQ • Lowest rate for the Not related category (0) • Highest proportion of 2+3 Related and helpful • Complementarity of tweets is not satisfying w.r.t. baselines Negative regression estimate of complementarity-based features in the collaboration likelihood model - phase A 19 / 30
  • 33. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. • Testing whether those provided tweets allow building a cohesive answer. Sandy Ebola 10 15 20 25 30 35 Avg Percentage of selected tweets Sandy Ebola 10 20 30 40 50 60 70 80 Number of built answers MMR U CRAQ-TR SM STM CRAQ • U: highest rate of selected tweets / lowest number of built answers 20 / 30
  • 34. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ1: Do the tweets posted by the collaborative group members recommended by the CRAQ allow the building of an answer? • Testing whether the CRAQ is effective in providing useful tweets in terms of relatedness to the question topic and complementarity. • Testing whether those provided tweets allow building a cohesive answer. Sandy Ebola 10 15 20 25 30 35 Avg Percentage of selected tweets Sandy Ebola 10 20 30 40 50 60 70 80 Number of built answers MMR U CRAQ-TR SM STM CRAQ • U: highest rate of selected tweets / lowest number of built answers • CRAQ: Lack of tweet complementarity does not impact the ability of the recommended group to answer the query 20 / 30
  • 35. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ2: Are the recommended group-based answers relevant? • Testing the relevance of the built answers MMR U CRAQ-TR SM STM CRAQ Sandy ba 43 29 75 74 67 77 1 11: 25.58% 9: 31.03% 23: 30.67% 24: 32.43% 21: 31.34% 17: 22.08% 2 20: 46.51% 14: 48.28% 33: 44.00% 34: 45.95% 24: 35.82% 39: 50.65% 3 12: 27.91% 6: 20.69% 19: 25.33% 16: 21.62% 22: 32.84% 21: 27.27% 2+3 32: 74.42% 20: 68.97% 52: 69.33% 50: 67.57% 46: 68.66% 60: 77.92% Ebola ba 22 11 39 30 37 41 1 4: 21.05% 3: 27.27% 15: 38.46% 8: 26.67% 15: 40.54% 10: 24.39% 2 6: 31.58% 4: 36.36% 18: 46.15% 15: 50% 16: 43.24% 22: 53.66% 3 9: 47.37% 4: 36.36% 6: 15.38% 7: 23.33% 6: 16.22% 9: 21.95% 2+3 15: 78.95% 8: 72.72% 24: 1.53% 22: 73.33% 22:59.46% 31: 75.61% • CRAQ enables to build a higher number of answers, among them a higher proportion of Partly relevant and Relevant answers U: reinforces our intuition that a single user might have an insufficient knowledge (even if related) to solve a tweeted question. 21 / 30
  • 36. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ2: Are the recommended group-based answers relevant? • Testing the relevance of the built answers MMR U CRAQ-TR SM STM CRAQ Sandy ba 43 29 75 74 67 77 1 11: 25.58% 9: 31.03% 23: 30.67% 24: 32.43% 21: 31.34% 17: 22.08% 2 20: 46.51% 14: 48.28% 33: 44.00% 34: 45.95% 24: 35.82% 39: 50.65% 3 12: 27.91% 6: 20.69% 19: 25.33% 16: 21.62% 22: 32.84% 21: 27.27% 2+3 32: 74.42% 20: 68.97% 52: 69.33% 50: 67.57% 46: 68.66% 60: 77.92% Ebola ba 22 11 39 30 37 41 1 4: 21.05% 3: 27.27% 15: 38.46% 8: 26.67% 15: 40.54% 10: 24.39% 2 6: 31.58% 4: 36.36% 18: 46.15% 15: 50% 16: 43.24% 22: 53.66% 3 9: 47.37% 4: 36.36% 6: 15.38% 7: 23.33% 6: 16.22% 9: 21.95% 2+3 15: 78.95% 8: 72.72% 24: 1.53% 22: 73.33% 22:59.46% 31: 75.61% • CRAQ enables to build a higher number of answers, among them a higher proportion of Partly relevant and Relevant answers U: reinforces our intuition that a single user might have an insufficient knowledge (even if related) to solve a tweeted question. MMR: gives rise to the benefit of building answers from the users perspective rather than the tweets regardless of their context. 21 / 30
  • 37. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ2: Are the recommended group-based answers relevant? • Testing the relevance of the built answers MMR U CRAQ-TR SM STM CRAQ Sandy ba 43 29 75 74 67 77 1 11: 25.58% 9: 31.03% 23: 30.67% 24: 32.43% 21: 31.34% 17: 22.08% 2 20: 46.51% 14: 48.28% 33: 44.00% 34: 45.95% 24: 35.82% 39: 50.65% 3 12: 27.91% 6: 20.69% 19: 25.33% 16: 21.62% 22: 32.84% 21: 27.27% 2+3 32: 74.42% 20: 68.97% 52: 69.33% 50: 67.57% 46: 68.66% 60: 77.92% Ebola ba 22 11 39 30 37 41 1 4: 21.05% 3: 27.27% 15: 38.46% 8: 26.67% 15: 40.54% 10: 24.39% 2 6: 31.58% 4: 36.36% 18: 46.15% 15: 50% 16: 43.24% 22: 53.66% 3 9: 47.37% 4: 36.36% 6: 15.38% 7: 23.33% 6: 16.22% 9: 21.95% 2+3 15: 78.95% 8: 72.72% 24: 1.53% 22: 73.33% 22:59.46% 31: 75.61% • CRAQ enables to build a higher number of answers, among them a higher proportion of Partly relevant and Relevant answers U: reinforces our intuition that a single user might have an insufficient knowledge (even if related) to solve a tweeted question. MMR: gives rise to the benefit of building answers from the users perspective rather than the tweets regardless of their context. CRAQ-TR (best baseline): building a group by gathering individual users identified as relevant through their skills (tweet topical similarity with the question) is not always appropriate. 21 / 30
  • 38. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context 22 / 30
  • 39. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group 22 / 30
  • 40. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group • CRAQ-TR: no significant differences in effectiveness / lower ratio of relevant answers. Benefit of the group entropy maximization based on the collaboration likelihood. 22 / 30
  • 41. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group • CRAQ-TR: no significant differences in effectiveness / lower ratio of relevant answers. Benefit of the group entropy maximization based on the collaboration likelihood. • SM: benefit of overpassing strong ties (users’ local network) to select relevant strangers 22 / 30
  • 42. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion EXPERIMENTAL EVALUATION RESULTS RQ3: What is the synergic effect of the CRAQ-based collaborative answering methodology? • Measuring the synergic effect of simulated collaboration within the recommended groups of users with respect to the search effectiveness based on the tweets published by those group members. MMR U CRAQ-TR SM STM CRAQ Value %Chg Value %Chg Value %Chg Value %Chg Value %Chg Value Sandy Precision 0.24 +92.93** 0.46 +2.32 0.33 +42.01* 0.21 +124.71***0.49 -4.1 0.47 Recall 0.09 +95.19* 0.1 +81.63* 0.15 +16.18 0.09 +105.96* 0.1 +80.09* 0.18 F-measure 0.12 +78.22* 0.15 +41.79 0.19 +10.59 0.12 +84.42* 0.15 +40.48 0.21 Ebola Precision 0.22 +153.65***0.64 -12.12 0.5 +12.22 0.3 +89.59** 0.45 +24.5 0.57 Recall 0.07 +155.59***0.11 +69.96* 0.22 -18.08 0.12 +46.80 0.06 +216.56***0.18 F-measure 0.09 +164.17***0.21 +17.46 0.28 -11.64 0.17 +50.07 0.1 +159.07***0.25 • MMR: sustains observed analysis on the lack of user context • U: consistent with previous work highlighting the synergic effect of a collaborative group • CRAQ-TR: no significant differences in effectiveness / lower ratio of relevant answers. Benefit of the group entropy maximization based on the collaboration likelihood. • SM: benefit of overpassing strong ties (users’ local network) to select relevant strangers • STM: topically relevant tweets issued from the most socially authoritative are not obviously relevant to answer the tweeted question. 22 / 30
  • 43. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion PLAN 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion 23 / 30
  • 44. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONCLUSION AND PERSPECTIVES Discussion • Novel method for answering questions on social networks: recommending a group of socially active and complementary collaborators. • Relevant factors: Information gain provided by a user to the group Complementarity and topical relevance of the related tweets Trust and authority of the group members • Method applicable for other social platforms (Facebook, community Q&A sites, ...) 24 / 30
  • 45. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion CONCLUSION AND PERSPECTIVES Discussion • Novel method for answering questions on social networks: recommending a group of socially active and complementary collaborators. • Relevant factors: Information gain provided by a user to the group Complementarity and topical relevance of the related tweets Trust and authority of the group members • Method applicable for other social platforms (Facebook, community Q&A sites, ...) Future Directions • Limitation of the predictive model of collaboration likelihood relying on basic assumptions of collaborations (mentions, replies, retweets). Deeper analysis of collaboration behavior on social networks to identify collaboration patterns. • Automatic summarization of candidate answers to build a collaborative answer. 24 / 30
  • 46. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion THANK YOU! @LaureSoulier @LyndaTamine @ngiahung 25 / 30
  • 47. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES I Balog, K., Fang, Y., de Rijke, M., Serdyukov, P., and Si, L. (2012). Expertise retrieval. Foundations and Trends in Information Retrieval, 6(2-3):127–256. Berberich, K. and Bedathur, S. (2013). Temporal Diversification of Search Results. In SIGIR #TAIA workshop. ACM. Cao, C., Caverlee, J., Lee, K., Ge, H., and Chung, J. (2015). Organic or organized?: Exploring URL sharing behavior. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pages 513–522. Carbonell, J. and Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the Annual International SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’98, pages 335–336. ACM. Chang, S. and Pal, A. (2013). Routing questions for collaborative answering in community question answering. In ASONAM ’13, pages 494–501. ACM. Ehrlich, K. and Shami, N. S. (2010). Microblogging inside and outside the workplace. In Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010. Evans, B. M. and Chi, E. H. (2010). An elaborated model of social search. Information Processing & Management (IP&M), 46(6):656–678. 26 / 30
  • 48. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES II Fuchs, C. and Groh, G. (2015). Appropriateness of search engines, social networks, and directly approaching friends to satisfy information needs. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, pages 1248–1253. Gong, Y., Zhang, Q., Sun, X., and Huang, X. (2015). Who will you ”@”? pages 533–542. ACM. Harper, F. M., Raban, D. R., Rafaeli, S., and Konstan, J. A. (2008). Predictors of answer quality in online q&a sites. In Proceedings of the 2008 Conference on Human Factors in Computing Systems, CHI 2008, 2008, Florence, Italy, April 5-10, 2008, pages 865–874. Haveliwala, T. H. (2002). Topic-sensitive PageRank. In Proceedings of the International Conference on World Wide Web, WWW ’02, pages 517–526. ACM. Hecht, B., Teevan, J., Morris, M. R., and Liebling, D. J. (2012). Searchbuddies: Bringing search engines into the conversation. In WSDM ’14. Honey, C. and Herring, S. (2009). Beyond Microblogging: Conversation and Collaboration via Twitter. In HICSS, pages 1–10. Horowitz, D. and Kamvar, S. D. (2010). The Anatomy of a Large-scale Social Search Engine. In WWW ’10, pages 431–440. ACM. 27 / 30
  • 49. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES III Jeffrey M. Rzeszotarski, M. R. M. (2014). Estimating the social costs of friendsourcing. In Proceedings of CHI 2014. ACM. Jeong, J.-W., Morris, M. R., Teevan, J., and Liebling, D. (2013). A crowd-powered socially embedded search engine. In ICWSM ’13. AAAI. Liu, Z. and Jansen, B. J. (2013). Factors influencing the response rate in social question and answering behavior. In Computer Supported Cooperative Work, CSCW 2013, pages 1263–1274. Mahmud, J., Zhou, M. X., Megiddo, N., Nichols, J., and Drews, C. (2013). Recommending targeted strangers from whom to solicit information on social media. In IUI ’13, pages 37–48. ACM. McNally, K., O’Mahony, M. P., and Smyth, B. (2013). A model of collaboration-based reputation for the social web. In ICWSM. Morris, M. R. (2013). Collaborative Search Revisited. In Proceedings of the Conference on Computer Supported Cooperative Work, CSCW ’13, pages 1181–1192. ACM. Morris, M. R., Teevan, J., and Panovich, K. (2010). What do people ask their social networks, and why?: a survey study of status message q&a behavior. In Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, pages 1739–1748. 28 / 30
  • 50. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES IV Nushi, B., Alonso, O., Hentschel, M., and Kandylas, V. (2015). Crowdstar: A social task routing framework for online communities. In ICWE ’15, pages 219–230. Pal, A. and Counts, S. (2011). Identifying topical authorities in microblogs. In WSDM ’11, pages 45–54. ACM. Paul, S. A., Hong, L., and Chi, E. H. (2011). Is twitter a good place for asking questions? A characterization study. In Proceedings of the Fifth International Conference on Weblogs and Social Media. Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1(1):81–106. Ranganath, S., Wang, S., Hu, X., Tang, J., and Liu, H. (2015). Finding time-critical responses for information seeking in social media. In 2015 IEEE International Conference on Data Mining, ICDM 2015, pages 961–966. Rzeszotarski, J. M., Spiro, E. S., Matias, J. N., Monroy-Hern´andez, A., and Morris, M. R. (2014). Is anyone out there?: unpacking q&a hashtags on twitter. In CHI Conference on Human Factors in Computing Systems, CHI’14, pages 2755–2758. Sonnenwald, D. H., Maglaughlin, K. L., and Whitton, M. C. (2004). Designing to support situation awareness across distances: an example from a scientific collaboratory. Information Processing & Management (IP&M), 40(6):989–1011. 29 / 30
  • 51. 1. Context and motivations 2. Related Work 3. The CRAQ Model 4. Experimental Evaluation 5. Conclusion REFERENCES V Soulier, L., Shah, C., and Tamine, L. (2014). User-driven System-mediated Collaborative Information Retrieval. In SIGIR ’14, pages 485–494. ACM. Tamine, L., Soulier, L., Jabeur, L. B., Amblard, F., Hanachi, C., Hubert, G., and Roth, C. (2016). Social media-based collaborative information access: Analysis of online crisis-related twitter conversations. In HT ’16. Teevan, J., Ramage, D., and Morris, M. R. (2011). #twittersearch: a comparison of microblog search and web search. In Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, pages 35–44. Wang, B., Wang, C., Bu, J., Chen, C., Zhang, W. V., Cai, D., and He, X. (2013). Whom to mention: Expand the diffusion of tweets by @ recommendation on micro-blogging systems. In Proceedings of the 22Nd International Conference on World Wide Web, WWW ’13, pages 1331–1340. ACM. 30 / 30