1. Cross-Platform Profiling
Workshop Digital Methods Summer School 2013
Carolin Gerlitz
University of Amsterdam/Goldsmiths, University of London

2. What is profiling?
• Specific approach in issue mapping.
• Issue mapping as the study of topical affairs.
• Cross-platform profiling studies issues and their
variation across multiple online spaces.
• In which platforms do issues occur?
• What actor composition?
• What thematic framing?
• Pace, rhythm, variation over time.

3. Profiling & Issue Mapping
• Studying topical affairs.
• Issue mapping, or controversy analysis, has been
developed as a research method in the field of
Science, Technology and Society (STS). (Callon,
1986; Barry, 2001; Latour, 2007)
• Empirical, processual approach.
• Asks: Is this topic an issue? Who are the actors?
Where is it based? Where is the issue happening?
How does it change?

4. Issue Profiling Online
• Digitization offers opportunities for
issue/controversy analysis (Rogers &
Marres 2000, Latour et. al 2007,
2010; Yaneva 2007):
• Explosion of digital traces and
analytical devices deploying
traceability.
• A special focus on taking advantage of
medium-specificity for profiling.

5. Profiling & specificity
• Interested in the specific articulation of issues
within and across different platforms.
• What are the specific forms of participation per
platform?
• Starting point: medium- and platform specifity.
• Deploy the pre-structured character of platforms
for analytical purposes (Marres & Weltevrede
2012).
• Media vs. issue dynamics.

6. Taking grammars of action into
account
• Which grammars of action/data entities (Agre
1994) are relevant for profiling?
• Google: Search results, hosts, titles.
• Twitter: Queries, hashtags, users, mentions.
• Facebook: Groups, Pages, Likes, Posts, user
relations.
• Flickr: Pictures, tags, groups.
• Wikipedia: Articles, related articles, edits, users…
Standardised activities & data forms cater to a
multiplicity of use practices.

7. Profiling
1. Actor composition.
2. Key platforms.
3. Bias and leaning.
4. Issue framing.
5. Variation over time.
6. Media effects.
7. Social dyanmics.

8. Examples
1. Cross-spherical profiling
2. Profiling issue variation over
time.
3. Hashtag Profiling
4. Associational Profiling.

9. Case 1 Cross-platform
profiling of Fukushima
• How is the issue of the Fukushima
nuclear disaster discussed in the web,
blog and news sphere?
• Word frequency analysis of Google
results.
• Do comments offer a different framing
of the issue?
1. Query Fukushima ~nuclear
2. Take top 25 URLs from Google Web,
Google Blogs, Google News
3. Copy paste content from URLs
4. Create a tagcloud

10. • Scraping as systematic extraction
of pre-formated data.
Google Scraper

11. Google Web
Google Web Comments

12. Google Blog
Google Blog Comments

13. Google News
Google News Comments

14. • Insights: different framing per sphere,
which sub-issues are trending right now.
• Q1 What about variation over time?
• Q2 Are there other measures beyond
word frequency?

15. Case 2 Variation over time
• Currency (liveness): Which aspects of
issues are peaking/trending at the
moment?
• Currency: word frequency analysis
• Variation over time (liveliness): changes
in association & framing of issues?
• Variation: Co-occurrence & co-word
analysis.
• Network analysis of textual data: tracing
relations between terms based on ‘co-
occurrence’ (Callon et al. 1983,
Danowski 2009).

16. Case 2 Profiling Crisis
• Interest in topical framing of crisis/austerity
across platforms (Marres & Weltevrede
2012).
• Mapping the liveness (currency) vs. liveliness
(variation over time) of “crisis” in Google and
Twitter.
• Early 2012.
• Google: Take search result titles into account.
• Twitter: Take hashtags into account.

18. Google & “crisis”

20. Twitter & “crisis”

21. Variation of Google
results over time

22. Case 3 Profiling Hashtags
• Intra-platform profiling of hashtags.
• Starting point: Hashtags as specific grammar
of action to demarcate conversations.
• How can we further profile hashtags and give
account to their liveliness?
• Case: Twitter data on Climate Change.
• Period: 01.02. - 15.06.2012, 204795 tweets.

24. Hashtag profiling.

25. Hashtag profiling.

26. Case 4: Associational
Profiling
• Using co-word to detect
associational profiles:
• Which words co-occur with a
given issue term on Twitter
across intervals?
• Stable or fluctuating
associations?

27. Associational Profiling

28. Associational Profiling

29. Associational Profiling

30. Associational Profiling

31. Profiling WCIT
• WCIT conference in Dubai, 3-14
Dec 2012 organised by the ITU.
• Interval: 23.11 – 19.12.
• 108781 Tweets.
• Before data capture: Collection of
NGO and issue expert hashtags.
• Question: How does the issue vary
over time and whose terms are
being taken up or not?

32. TCAT Associational
Profiler
• Part of the Digital Methods Toolkit.
• Name: dmi
• Password: twitter

33. TCAT WCIT dataset

34. #WCIT Profile
• Campaign Hashtags: #netfreedom,
#ituvideo, #anonymous,
#opbigbrother.
• Institutional Hashtags: #isoc,
#ican.
• Campaign issues dominated at the
beginning, institutional ones at the
end.
• http://issuemapping.net/Main/
WCITProfile

35. Before the conference
• #netneutrality
• Pushed before the conference,
weaker associations during.

36. Start of the conference
• #anonymous
• Campaign hashtag.

37. End of the conference
• #isoc
• Institutional hashtag.

38. • #privacy
• Expert hashtag.
• Very diverse profile.
Varying associations

39. Varying associations
• #netneutrality
• Expert hashtag.
• Declining presence and less
diverse associations.

40. Emergent questions
• How does media liveliness map
into issue liveliness? How is
variation defined by grammars of
action?
• Media-liveliness: bursty hashtags,
hashtag decline.
• Can we perceive medium- and
issue-specificity as a spectrum?
• Further research: How can we
profile social dynamics?
• What makes an issue more or less
social?

41. Exercise
Profiling Prism

42. Profiling Prism
• Go to DMI TCAT tool.
• Select “prism” dataset.
• Adjust the time interval to one
day.
• Open Associational Profile in
Experimental Analytics.
• Create profile for term
“prism” or “spying”.
• Interval: Weekly.
• Exclude: prism, snowden.
• Which words to stand out?
Create further profiles for
detailed analysis.

43. Profiling “spying”

44. • What do we see or not in associational profiles?
• How to profile prism in other platforms?
• How to create a social profile, focused on the
social dynamics of the specific platforms?
Questions