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Information Retrieval and Social Media

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Lecture on Information Retrieval and Social Media, given to PhD students in the User-Centred Social Media Summer School, in Duisburg, September 19, 2017.

See also https://www.ucsm.info/events/118-new-frontiers-in-social-media-research-%E2%80%93-international-summer-school-2018

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Information Retrieval and Social Media

  1. 1. Information Retrieval and Social Media Prof.dr.ir. Arjen P. de Vries arjen@acm.org Lecture for the User-Centred Social Media Summer School Duisburg, September 19, 2017
  2. 2. Social Media Noun social media (uncountable) Interactive forms of media that allow users to interact with and publish to each other, generally by means of the Internet. The early 21st century saw a huge increase in social media thanks to the widespread availability of the Internet.
  3. 3. Social Media  “Social bookmarking” sites  “User generated content” - Images (flickr) and videos (youtube, vimeo), but also blogs, Wikipedia, etc.  Social network services - Twitter, facebook, instagram, snapchat
  4. 4. Not just one beast!
  5. 5. User contributed content
  6. 6. Permission based tagging, Set model
  7. 7. Bag model Global Content Free for all tagging
  8. 8. Social Media to help improve IR (1)
  9. 9. ‘Co-creation’  Social Media: - Consumer becomes a co-creator - Many ‘data consumption’ traces in social media are public
  10. 10. Richer information representations
  11. 11. Richer information representations  User profiles - User name, full name, description, image, homepage url, etc.  Connections between users - Networks of friends, followers, etc  Comments/reactions  Endorsing and sharing
  12. 12. E.g., Twitter  Bio - Often includes a geo-location of the profile  Friends  Followers  Lists - Groups followed Twitter accounts; lists can be followed  Hashtags  Mentions
  13. 13. User Demographics  Gender from Tweet author’s first name  Geographic location from profile Diaz, Gamon, Hofman, Kiciman, Rothschild. Online and Social Media as an Imperfect Continuous Panel Survey. In PLOS ONE, 2016
  14. 14. Detailed User Characteristics… de Volkskrant, March 13, 2013 Michal Kosinski, David Stillwell, and Thore Graepel. Private traits and attributes are predictable from digital records of human behavior. PNAS 2013. Youyou, W., Kosinski, M. & Stillwell, D. (2015) Computer-based personality judgments are more accurate than those made by humans. PNAS 2015.
  15. 15. … in Search  Age and Gender, and perhaps also political and religious views  Maps both Page Likes from myPersonality dataset and search results on a common space of ODP categories  Learning approach to overcome the difference in distribution between myPersonality data and Search data - E.g., their FB dataset has 63% female, vs. only 47% in Bing Bi, Kosinski, Shokouhi, Graepel. Inferring the Demographics of Search Users. WWW 2013
  16. 16. Many Opportunities for IR  Expand content representation  Reduce the vocabulary gap(s) between creators of content (the indexers) and consumers of content (the users)  More diverse views on the same content
  17. 17. LibraryThing  Items  People  Tags  Ratings
  18. 18. Synonyms
  19. 19. Synonyms Dissimilar users… … with similar items (Pearson Correlation) Note: this representation ignored the item ratings
  20. 20. Examples • Humour • Classic
  21. 21. IR to help improve Social Media
  22. 22. LibraryThing – beyond terms  Items  People  Tags  Ratings
  23. 23. Maarten Clements, Arjen P. de Vries and Marcel J.T. Reinders. The task dependent effect of tags and ratings on social media access. TOIS 28, 4, article 21 (November 2010), 42 pages.
  24. 24. Search with Random Walk  Present nodes according to estimated probability that a random walk that starts from (task dependent) starting nodes, would end at this node
  25. 25. Tagging Relationships
  26. 26. Note: this representation used the item ratings in the user – item transitions
  27. 27. An item recommendation walk
  28. 28. Personalized Search  Assume a user who types a single tag as query
  29. 29.  A soft clustering effect smoothly relates similar concepts before converging to the background probability
  30. 30.  Homographs like “Java” are disambiguated because the walk starts in both the query tag and the target user - So, content that matches the user’s preference is more likely to be found first
  31. 31. Expert Finding on Twitter  Empirical evidence demonstrates that a mix of tweet text, friends, followers and lists is most effective to infer expertise  Expertise ground truth taken from Quora, where (many) users list their expertise and their social media accounts Xu, Zhou and Lawless. Inferring your expertise from Twitter: combining multiple types of user activity. WI ‘2017
  32. 32. Multiple Social Networks  Accounts linked via services like about.me and Quora  Users explicitly list their multiple accounts in one profile  Missing data addressed via non-negative matrix factorization (NMF) - E.g., 57% list school in FB, 81% in LinkedIn  Applied to various prediction tasks, e.g., topics users are interesting in
  33. 33. Social Media to help improve IR (2)
  34. 34. Relevant for Search… (1/4)  Wikipedia contains semantically very rich annotations: - Wikipedia Categories, Lists - Times (1930, 1931, 1932, etc. etc.) - Disambiguation pages - Edit history Etc. Note: DBPedia is “just” Wikipedia 
  35. 35. Relevant for Search… (2/4)  “Twanchor text” - Tweets citing online media can be used as additional resources describing the content, just like anchor text
  36. 36. Relevant for Search… (3/4)  Geotags / POIs - Recommend geo-locations to people - Recommend people to geo-locations - Predict a user’s whereabouts (or “trails”)
  37. 37. Relevant for Search… (4/4)  Timestamps - Helps reveal trends, e.g., which documents went viral? - Allows to search “in the past”
  38. 38. Searching the Social Web  Do not improve Web search with social annotations, but improve search in Social  Builds on the observation in prior work (Goel et al., 2016) that virality is really different from popularity - The most viral content is often distinct from the most popular content being shared online - Can we surface that content more easily? Alonso, Kandylas, Tremblay, Hofman, Sen. What’s Happening and What Happened: Searching the Social Web. WebSci ‘17.
  39. 39. Pipeline  Content selection: - Select tweets that contain links and satisfy simple user, content and time range criteria  User selection: - Extract and normalize links and select those that have been shared by a minimum number of trusted users  Link selection: - Clean-up links, compute link virality and popularity, cluster similar links, and apply heuristic criteria to select good quality links  Annotations: - Generate metadata for the selected links from the associated tweets
  40. 40. Collecting Data
  41. 41. API Blues Bit.ly API used in my own research: /v3/link/content deprecated Note: This endpoint was deprecated on 10/15/2014.
  42. 42. API Blues  The combination of rate limits and Terms of Service of most social media platforms complicates our life  Not even to mention volume - TREC Microblog collection of 2013 “Tweets2013” consists of 107 GB compressed (for only 2 months of data!)  Did I mention ToS? - Mandatory continual processing of deletions…
  43. 43. Good News for Twitter  The Internet Archive distributes two collections from 2013 that can be used as drop-in replacement for evaluation purposes  Deletions seem to affect non-relevant documents more than relevant documents Sequira and Lin. Finally, a Downloadable Test Collection of Tweets. SIGIR 2017.
  44. 44. Social Media as Panel Survey  Online population is a non-representative sample of the off-line world  Demographic skew and user participation is non- stationary and difficult to predict over time - E.g., women are underrepresented in the raw volume of tweets, but tweet more often about politics than men - Half of the activity on a specific debate came from individuals who had not previously posted about the election Diaz, Gamon, Hofman, Kiciman, Rothschild. Online and Social Media as an Imperfect Continuous Panel Survey. In PLOS ONE, 2016
  45. 45. Fred Morstatter, Jürgen Pfeffer, Huan Liu and Kathleen M. Carley. Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose. ICWSM 2013 API Blues
  46. 46. Take home message(s)
  47. 47. Take home message(s) • Social media give access to a rich resource of context - Including time & location!
  48. 48. Take home message(s) • Social media give access to a rich resource of context - Including time & location! • The academic’s alternative to click data?
  49. 49. Take home message(s) • Social media give access to a rich resource of context - Including time & location! • The academic’s alternative to click data? • A big open research question: Can one theory (about matching users and content) address the complete spectrum of IR tasks that arise in social media?

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