Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
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Twitter analytics: some thoughts on sampling, tools, data, ethics and user requirements
1. Twitter analytics: some thoughts
on sampling, tools, data, ethics
and user requirements
Farida Vis, Information School
University of Sheffield
@flygirltwo
Keynote SRA Social Media in Social Research conference, London, 24 June 2013.
2. READING
THE RIOTS
ON TWITTER
Rob Procter (University of Manchester)
Farida Vis (University of Leicester)
Alexander Voss (University of St Andrews)
[Funded by JISC]
#readingtheriots
3. What role did social media play?
2.6 million riot tweets (donated by Twitter)
–
700,000 individual accounts
Initially:
o Role of Rumours
o Did incitement take place? [no - #riotcleanup]
o What is the role of different actors on Twitter?
5. Guardian Interactive Team (Alastair Dant)
http://www.guardian.co.uk/uk/interactive/20
11/dec/07/london-riots-twitter
Data Journalism Award (sponsored by
Google)
6.
7.
8.
9. • Lots of questions about methods
• Lots of questions about our tools
• Lots of questions about donated data
• Lots of questions about ethics
11. Actor Types – top 1000 mentions
Typical long tail distribution
Twitter researchers tend to focus on the head
12. Actor Types
Mainstream Media Police/emergency services
Only online media (news) Riot accounts
Non-(news) mainstream media Celebrities
Journalists (mainstream media) Researchers
Journalists (online media) Members of the public
Non-(news) media organisations Bots
Bloggers Unclear
Activists Account closed down
UK Twitterati Fake/spoof account
Political Actors Other
http://researchingsocialmedia.org/2012/01/24/reading-the-riots-on-twitter-who-tweeted-the-riots/
13. Who tweeted the riots? - categories
mainstream media
journalists
riot accounts
14. You know you’re dealing with Twitter data when…
Number 13, 6697 mentions
Number 20, 5939 mentions
Number 23, 5527 mentions
17. 30031 mentions, 441 tweets sent over 4 days: top UK listed journalist (2)
3484 mentions, 290 tweets sent over 4 days: top non UK listed journalist
(34)
30. Collecting the data
Scraper by Jacopo
Ottaviani
URL for the scraper: https://scraperwiki.com/scrapers/police_and_the_olympics_2012/
ScraperWiki is a key DDJ
site
32. Data challenges
• Collecting Twitter data in (real) time (APIs)
• Methods for building a reliable corpus
• Problems with language bias
• Problems with hashtag/keyword bias
• API bias
• Demographics of Twitter users – who are they?
• Problems with escalating volume
• Mapping explosion of new tools: are they any good?
• Off the shelf tools (growing divide in research capacity in
this area)
• Limitations of the tools
• Problems with data sharing / replicating studies + findings
40. We collect and analyse messages exchanged in Twitter using two of
the platforms publicly available APIs (the search and stream
specifications). We assess the differences between the two samples,
and compare the networks of communication reconstructed from them.
The empirical context is given by political protests taking place in May
2012: we track online communication around these protests for the
period of one month, and reconstruct the network of mentions and re-
tweets according to the two samples. We find that the search API over-
represents the more central users and does not offer an accurate
picture of peripheral activity; we also find that the bias is greater for the
network of mentions. We discuss the implications of this bias for the
study of diffusion dynamics and collective action in the digital era, and
advocate the need for more uniform sampling procedures in the study
of online communication.
(González-Bailó n et al, 2012)
42. Random sampling with the streaming API: the 1%
‘If we estimate a daily tweet volume of 450 million tweets (Farber), this
would mean that, in terms of standard sampling theory, the 1%
endpoint would provide a representative and high resolution sample
with a maximum margin of error or 0.06 as a confidence level of 99%,
making the study of even relatively small subpopulations within that
sample a realistic option.’
(Gerlitz and Rieder, 2013)
44. ‘The essential drawback of the Twitter API is the lack of documentation
concerning what and how much data users get. This leads researchers
to question whether the sampled data is a valid representation of the
overall activity on Twitter. In this work we embark on answering this
question by comparing data collected using Twitter’s sampled API
service with data collected using the full, albeit costly, Firehose stream
that includes every single published tweet.’
(Morstatter et al, 2013)
47. For hashtag datasets: contributions made by specific users and
groups of users; overall patterns of activity over time;
combinations to examine contributions by specific users and
groups over time. (Bruns and Stieglitz, 2013)
84. DMI tools for extracting links (all the URLs)
Mostly URLS are shorted, mainly using t.co (Twitter). Unpack them using:
Didn’t always work, manual unpacking and note taking (plus you still
have the shortened URL in case you want to retrace it.
95. For hashtag datasets: contributions made by specific users and
groups of users; overall patterns of activity over time;
combinations to examine contributions by specific users and
groups over time. (Bruns and Stieglitz, 2013)
102. What do we want from these APIs, the data,
the tools, and Twitter researchers so that we
can develop more robust social scientific
research on Twitter?
104. References
• Bruns, A., and Stieglitz, S. 2013. Towards More Systematic Twitter Analysis: Metrics
for Tweeting Activities. International Journal of Social Research Methodology.
DOI:10.1080/13645579.2013.770300 Available from:
http://snurb.info/files/2013/Towards%20More%20Systematic%20Twitter%20Analysis
%20(final).pdf
• Gerlitz, C. & Rieder, B. 2013. Mining One Percent of Twitter: Collections, Baselines,
Sampling. M/C Journal, Vol. 16, No 2. Available from: http://journal.media-
culture.org.au/index.php/mcjournal/article/viewArticle/620
• González-Bailó n, S., Ning, W., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. 2012.
Assessing the Bias in Communication Networks Samples from Twitter. Available
from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2185134
• Morstatter, F., Pfeffer, J., Liu, H, & Carley, K.M. 2013. Is the Sample Good Enough?
Comparing Data from Twitter’s Streaming API with Twitter’s Firehose. Association for
the Advancement of Artificial Intelligence. Available from:
http://www.public.asu.edu/~fmorstat/paperpdfs/icwsm2013.pdf
• Vis, F. 2012 . Twitter as a reporting tool for breaking news: journalists tweeting the
2011 UK riots, Digital Journalism 1(1). Available from:
http://www.tandfonline.com/doi/full/10.1080/21670811.2012.741316#.UcwBZ-CPDao
• Vis, F., Faulkner, S., Parry, K., Manyukhina, Y., and Evans, L. (in press), Twitpic-ing
the riots: analysing images shared on Twitter during the 2011 UK riots, in Twitter and
Society, Weller, K., Bruns, A., Burgess, J.,Mahrt, M., and Puschmann, C. (eds.), New
York: Peter Lang.