Social media provides a rich source of contextual information for improving information retrieval, including user profiles, connections between users, comments, and shared content. However, using social media for research poses challenges due to limitations of APIs, non-representative user samples, and data that is constantly changing. Despite these issues, social media still offers opportunities to expand content representation, reduce vocabulary gaps, and surface viral content that improves search capabilities.

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. 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. 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

8. Not just one beast!

9. User contributed content

10. Permission based tagging, Set model

11. Bag model
Global Content
Free for all tagging

12. Social Media to help improve IR (1)

13. ‘Co-creation’
 Social Media:
- Consumer becomes a co-creator
- Many ‘data consumption’ traces in social media are public

14. Richer information representations

15. 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

16. 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

17. 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

18. 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.

19. … 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

20. 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

21. LibraryThing
 Items
 People
 Tags
 Ratings

22. Synonyms

23. Synonyms
Dissimilar users…
… with similar items
(Pearson Correlation)
Note: this representation ignored the item ratings

25. Examples
• Humour
• Classic

26. IR to help improve Social Media

27. LibraryThing – beyond terms
 Items
 People
 Tags
 Ratings

28. 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.

29. 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

30. Tagging Relationships

31. Note: this representation used the item ratings in the user – item transitions

32. An item recommendation walk

33. Personalized Search
 Assume a user who types a single tag as query

34.  A soft clustering effect smoothly relates similar concepts
before converging to the background probability

35.  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

36. 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

37. 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

38. Social Media to help improve IR (2)

39. 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 

40. Relevant for Search… (2/4)
 “Twanchor text”
- Tweets citing online media can be used as additional resources
describing the content, just like anchor text

41. Relevant for Search… (3/4)
 Geotags / POIs
- Recommend geo-locations to people
- Recommend people to geo-locations
- Predict a user’s whereabouts (or “trails”)

42. Relevant for Search… (4/4)
 Timestamps
- Helps reveal trends, e.g., which documents went viral?
- Allows to search “in the past”

43. 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.

45. 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

47. Collecting Data

48. API Blues
Bit.ly API used in my own research:
/v3/link/content
deprecated
Note: This endpoint was deprecated on 10/15/2014.

49. 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…

50. 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.

51. 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

52. 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

53. Take home message(s)

54. Take home message(s)
• Social media give access to a rich resource of context
- Including time & location!

55. Take home message(s)
• Social media give access to a rich resource of context
- Including time & location!
• The academic’s alternative to click data?

56. 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?