User Taglines: Alternative Presentations of Expertise and Interest in Social Media


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Web applications are increasingly showing recommended users from social media along with some descriptions, an attempt to show relevancy - why they are being shown. For example, Twitter search for a topical keyword shows expert twitterers on the side for 'whom to follow'. Google+ and Facebook also recommend users to follow or add to friend circle. Popular Internet newspaper- The Huffington Post shows Twitter influencers/ experts on the side of an article for authoritative relevant tweets. The state of the art shows user profile bios as summary for Twitter experts, but it has issues with length constraint imposed by user interface (UI) design, missing bio and sometimes funny profile bio. Alternatively, applications can use human generated user summary, but it will not scale. Therefore, we study the problem of automatic generation of informative expertise summary or taglines for Twitter experts in space constraint imposed by UI design. We propose three methods for expertise summary generation- Occupation-Pattern based, Link-Triangulation based and User-Classification based, with use of knowledge-enhanced computing approaches. We also propose methods for final summary selection for users with multiple candidates of generated summaries. We evaluate the proposed approaches by user-study using a number of experiments. Our results show promising quality of 92.8% good summaries with majority agreement in the best case and 70% with majority agreement in the worst case. Our approaches also outperform the state of the art up to 88%. This study has implications in the area of expert profiling, user presentation and application design for engaging user experience.

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User Taglines: Alternative Presentations of Expertise and Interest in Social Media

  1. 1. “How do we know: Why are you influential?” ASE Social-Informatics 2012, Dec 16 Hemant Purohit1 Alex Dow, Omar Alonso, Lei Duan, Kevin Haas1Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State , USA Microsoft Silicon Valley, USA
  2. 2. Outline Motivation Problem Settings Proposed Approach Experiments & Results Future Work User Tagline, Social-Informatics 2012 2
  3. 3. Motivation HuffingtonPost• Increasing use of recommended influencers and experts from social media.• No relevance cues about who they are and why they are experts• Need presentation of their expertise or interest areas• Help user engagement with the content ? User Tagline, Social-Informatics 2012 3
  4. 4. Motivation Tagline Senior Media Reporter for The Huffington Post• I can see why … Analysis & Aggregation User Data Mining Better engagement with content! User Tagline, Social-Informatics 2012 4
  5. 5. Problem Statement Scope:  Twitter users with English language profile descriptions and tweets Specific problem:  Given a set of N experts E= {ei | i = 1, 2, . . . , N} in a social media community C of K (K > N) users, generate a short summary di within predefined length for each of the expert ei in the set E. User Tagline, Social-Informatics 2012 5
  6. 6. Challenges How to extract information from user’s Twitter profile bio that well presents the author’s expertise? Constraint space of UI design when displaying it. Meteorologist at TWC in Atlanta . Hike, sail, snowboard, travel, take pictures and glue stuff. User Tagline, Social-Informatics 2012 6
  7. 7. Challenges How to generate tagline when user’s bio doesn’t contain sufficient information?  Some profile bios are humorous.  Sometimes profile bio is simply missing User Tagline, Social-Informatics 2012 7
  8. 8. Challenges In difference with web search document summarization or snippet generation  Very short text segment vs. full page of web documents  People centric vs. document centric  More constraints on space due to UI design  Lack of or missing information User Tagline, Social-Informatics 2012 8
  9. 9. Proposed Approach Candidate Data Final Tagline Taglines Sampling Selection Generation User Tagline, Social-Informatics 2012 9
  10. 10. What Data to Exploit? Meformer  Self Descriptive  e.g., Profile Bio, Content of the user posts Informer  Others describe the person  e.g., Wikipedia, IMDB, Freebase etc. User Tagline, Social-Informatics 2012 10
  11. 11. Candidate Taglines Generation 1. Occupation-Patterns 2. Link-Triangulation 3. User Classification User Tagline, Social-Informatics 2012 11
  12. 12. 1. Occupation-Pattern ApproachOccupation well-represents user’s expertise areas. west coast editor for @thenextweb. geek. world changer. social good creator. community lover. #BlameDrewsCancerer. #ihaveadream. yep, that drew. Ufc Octagon Girl,Ultimate Insider Host. Model, Actress, Bud Light Spokesmodel and Fitness Nerd. Im Not that cool, Lover not a fighter. president and editor-in-chief of the huffington post media group. mother. sister. flat shoe advocate. sleep evangelist. User Tagline, Social-Informatics 2012 12
  13. 13. Extracting Occupation Patterns Occupation titles collection  from Wikipedia and  US Dept. of Labor Statistics Reports Given a user’s profile bio  Spot the occupation titles in the N-grams of the bio  Create candidate tagline by joining N-grams in the order of their appearance in profile bio (Intuition: User’s own described order of what he is expert for!)  Create final candidate set by those falling in the UI space constraint threshold. User Tagline, Social-Informatics 2012 13
  14. 14. What if there is no occupationalpattern? Can we exploit informer data?  Expert users usually have Wikipedia pages where people have written about them  Connect Twitter expert users with their Wikipedia pages.  Use Wikipedia informative summary as expert users tagline User Tagline, Social-Informatics 2012 14
  15. 15. 2. Link-Triangulation Approach User Wikipedia page’s Info-box Twitter User User Identity Resolution by cross referenced links Personal Website: User Tagline, Social-Informatics 2012 15
  16. 16. Extracting User Description After resolving user identity corresponding to a Knowledge-base (KB) page, exploit content of KB Wikipedia Infobox ‘occupation’ property  e.g., for celebrity TV host ‘Rachael Ray’: Television Personality, businesswoman, author, celebrity chef Apply restriction by UI space constraint User Tagline, Social-Informatics 2012 16
  17. 17. Link-Triangulation Examples USER BIO WIKIPEDIA BASED TAGLINE The legend Tommy Chong from the iconic comedy duo Tommy Cheech and Chong. Currently on the Get it legal North Chong American tour. actor, comedian, musician actor, comedian, producer, musician, Drake Bell Thanks for following me, guys! singer-songwriter, director, voice artist Proud husband, father, comedian, philanthropist and author. God has blessed me with more than I could have asked for. New Daytime TV Show premiering September actor, comedian, radio and televisionSteve Harvey 4 personality, author I like long walks on the beach...and then to be dragged through the sand by an off-road vehicle, and then hurled actor, comedian, television host, bob saget off a catapult. director, screenwriter User Tagline, Social-Informatics 2012 17
  18. 18. What happens if … No occupational-patterns can be extracted No links or can’t resolve links to knowledge base Can we classify users to categories based on their  Popularity  Activity  Content diffusion strength? Solution: Use the classes as default taglines if nothing else User Tagline, Social-Informatics 2012 18
  19. 19. 3. User Classes Approach USER POPULARITY Casual Disengaged Celebrity Celebrity Passive Active Celebrity Celebrity Casual Twitterer Conversationalist USER ACTIVITY Authority Information Hub DIFFUSION STRENGTH User Tagline, Social-Informatics 2012 19
  20. 20. User Classification Metrics Popularity, Activity and Diffusion Strength can be modeled in different ways Our formulation, for given dataset period:  Popularity- #user’s mentions  Activity- #statuses  Diffusion Strength- #retweets of user tweets 50% percentile on each metrics (max normalized) as classifier to create various classes in the 3-D model User Tagline, Social-Informatics 2012 20
  21. 21. User Classes ExamplesTWITTERSCREEN- DISPLAY- NAME NAME BIO PROFILE LINK USER-CLASS TAGLINE Steve Steve SullivanSullivan26 Sullivan EMPTY Unmatched with Wiki is Casual Twitterer. SHAH RUKH SRK is Active Celebrity iamsrk KHAN EMPTY Unmatched with Wiki on Twitter. User Tagline, Social-Informatics 2012 21
  22. 22. Proposed Approach Candidate Final Tagline Data Taglines Selection Sampling Generation User Tagline, Social-Informatics 2012 22
  23. 23. Principles for Quality Assessmentof a Tagline Readability ‘host of Man Vs Wild & Born Survivor, family man and host of Man Vs Wild’ vs. ‘chief scout (the scout association), adventurer, explorer, author, motivational speaking, television presenter’ Specificity  ‘Actor, Director’ vs. ‘Actor in Forest Gump’ Interestingness  ‘CEO of Amazon, Co-founder of ABC Corp., Actor, Writer’ User Tagline, Social-Informatics 2012 23
  24. 24. ReadabilityFlesch Readability Ease Scoring [0-100] - Higher the better CANDIDATE TAGLINE-1 SCORE CANDIDATE TAGLINE-2 SCORE singer, actor, songwriter, composer, singer from Norway 62.7 pianist 0 songwriter from Glasgow 34.5 singer, songwriter, musician 0 chief scout (the scout association), adventurer, explorer, author, host of Man Vs Wild & Born Survivor, motivational speaking, television family man and host of Man Vs Wild 84.4 presenter 0 Observations  Poor scores for fragmented sentences  Not suitable for measuring taglines  Skipped for the experimental stats User Tagline, Social-Informatics 2012 24
  25. 25. Specificity & Interestingness• Difficult to formulate specificity and interestingness of a tagline because of subjectivity• Modified Tf-idf approach: • query=user, relevant-docs-to-user= candidate taglines set • Document set as all the candidate taglines of all the users • Normalize score for a document (tagline) by its character length, w.r.t. maximum UI space constraint T (to boost the space utilization) • Rank candidate taglines scores for a user and then select final tagline User Tagline, Social-Informatics 2012 25
  26. 26. Specificity & Interestingness Examples: TAGLINE-1 SCORE-1 TAGLINE-2 SCORE-2 FINAL-TAGLINE singer, actor, songwriter, singer, actor, songwriter, singer from Norway 0.791859 composer, pianist 1.221946 composer, pianist songwriter from Glasgow 1.03031 singer, songwriter, musician 0.496351 songwriter from Glasgow Ultimate Insider Host,Ultimate Insider Host, Model, Bud Model, Bud Light Light Spokesmodel 3.179482 model, ufc octagon girl 1.429542 Spokesmodel chief scout (the scout chief scout (the scout host of Man Vs Wild & Born association), adventurer, association), adventurer, Survivor 2.109237 explorer, author 4.834862 explorer, author Multi Grammy Award-winning singer, actress, comedian, Multi Grammy Award- singer 1.423924 author, producer 0.897228 winning singer User Tagline, Social-Informatics 2012 26
  27. 27. Evaluation Baseline: Full Twitter profile bio as a tagline User study  Questions for user study of candidate tagline generation  How good is the generated summary of expertise presentation?  How good the original bio is by itself? User Tagline, Social-Informatics 2012 27
  28. 28. Evaluation (contd.) Metrics for evaluation  Majority agreement on good candidate taglines: Percentage of total samples, where at least two third of judges give scores as either 1 (good) or 2 (very good) for the question Q1  Inter-rater agreement (Fleiss Kappa) User study for final tagline selection  Q. Which one is the best summary to represent expertise description between two given candidate summaries?  Metric: % of agreeable summaries between human and automatic judgment User Tagline, Social-Informatics 2012 28
  29. 29. Experimental Data Sets• Crawled Twitter datasets from different time periods: tweets and users• Expert* Twitter Users set: Top 30% ranked users based on Klout score in the dataset collected *Note: that any Expert Finding Algorithm or service can be used, our approach is independent on that. Table I. STATISTICS OF EXPERT USERS IN THREE DATA SETS User Tagline, Social-Informatics 2012 29
  30. 30. User Study Results For candidate tagline generation: Majority Agreement for good Baseline Phase-1 Phase-2 taglines datasets datasets Occupation-Pattern approach 30% 74.5% 70.31% Link-Triangulation approach *30% 91.6% 92.82%  *for Link-Triangulation approach we did not have a good baseline than the user profile bio For final tagline selection:  Majority Agreement for final tagline choice between automatic and human judge selection: 65.15% User Tagline, Social-Informatics 2012 30
  31. 31. Conclusion The contribution of this work  Proposed the practical approach to represent user expertise on social media as taglines given constraints of UI design.  Addressed important information extraction from user profile bio by applying occupational-pattern approach.  Addressed quality and coverage issue of tagline generation by link-triangulation approach leveraging knowledge base such as Wikipedia.  Explored user classification approach for generating taglines for users without profile bios.  Experiment results show significant improvement over baseline of the state-of-art. User Tagline, Social-Informatics 2012 31
  32. 32. Future Work Exploit user’s web-page  Title and Keywords in the Header metadata Semantic approach in creating expert summary  Focusing on semantic association of the tagline parts with respect to domain specific application and thus, improve extraction and ordering of parts for expertise Topical classification of users Leverage web search results & snippets as knowledge source for users as queries User Tagline, Social-Informatics 2012 32
  33. 33. Acknowledgement We thank our colleagues at Microsoft and Kno.e.sis for fruitful feedback and discussions User Tagline, Social-Informatics 2012 33
  34. 34. Questions and thanks .. Thanks for hearing our presentation  For later questions?   Reference:  Hemant Purohit, Alex Dow, Omar Alonso, Lei Duan, Kevin Haas. User Taglines: Alternative Presentations of Expertise and Interest in Social Media. First ASE International Conference on Social Informatics (Social-Informatics 2012). User Tagline, Social-Informatics 2012 34