Cross-Platform Profiling

Workshop Digital Methods Summer School 2013

Carolin Gerlitz University of Amsterdam/Goldsmiths, University of London

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.

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?

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.

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.

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.

Profiling

1.  Actor composition.

2.  Key platforms.

3.  Bias and leaning.

4.  Issue framing.

5.  Variation over time.

6.  Media effects.

7.  Social dyanmics.

Examples

1.  Cross-spherical profiling

2.  Profiling issue variation over time.

3.  Hashtag Profiling

4.  Associational Profiling.

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

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

Google Web

Google Web Comments

Google Blog

Google Blog Comments

Google News

Google News Comments

•  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?

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).

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.

Google & “crisis”

Twitter & “crisis”

Variation of Google results over time

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.

Hashtag profiling.

Hashtag profiling.

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?

Associational Profiling

Associational Profiling

Associational Profiling

Associational Profiling

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?

TCAT Associational Profiler

•  Part of the Digital Methods Toolkit.

•  Name: dmi

•  Password: twitter

TCAT WCIT dataset

#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

Before the conference •  #netneutrality

•  Pushed before the conference, weaker associations during.

Start of the conference •  #anonymous

•  Campaign hashtag.

End of the conference •  #isoc

•  Institutional hashtag.

•  #privacy

•  Expert hashtag.

•  Very diverse profile. Varying associations

Varying associations •  #netneutrality

•  Expert hashtag.

•  Declining presence and less diverse associations.

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?

Exercise Profiling Prism

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.

Profiling “spying”

•  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