Twitter analytics: some thoughts on sampling, tools, data, ethics

and user requirements

Farida Vis, Information SchoolUniversity of Sheffield

@flygirltwo

Keynote SRA Social Media in Social Research conference, London, 24 June 2013.

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

What role did social media play?

2.6 million riot tweets (donated by Twitter) –

700,000 individual accounts

Initially:o Role of Rumourso Did incitement take place? [no - #riotcleanup]o What is the role of different actors on Twitter?

Role of Rumours

Guardian Interactive Team (Alastair Dant)

http://www.guardian.co.uk/uk/interactive/2011/dec/07/london-riots-twitter

Data Journalism Award (sponsored by Google)

• Lots of questions about methods

• Lots of questions about our tools

• Lots of questions about donated data

• Lots of questions about ethics

Actively engaged on Twitter

Actor Types – top 1000 mentionsTypical long tail distribution

Twitter researchers tend to focus on the head

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/

Who tweeted the riots? - categoriesmainstream media

journalists

riot accounts

You know you’re dealing with Twitter data when…

Number 13, 6697 mentions

Number 20, 5939 mentions

Number 23, 5527 mentions

Context

Context

Context

Individual accounts with > 3K mentions

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)

Image sharing practices during crises

400 million tweets/day (March 2013)

40 million Instagram images/day (January 2013)

Percentages posted to Twitter / Facebook

-> 59% posted to Twitter

-> 98% posted to Facebook

Where do images fit in the era of ‘Big Data’?

Big Data – text + number driven

Images: undervalued, underexplored

Not by the users

Deleted contenthttp://twitpic.com/62m6nx

#FakeSandy pics 250,000 tweets (4hrs) 1 weekend

http://istwitterwrong.tumblr.com/

Jean Burgess

Farida Vis

Axel Bruns

‘fakes’

http://www.guardian.co.uk/news/datablog/2012/nov/06/fake-sandy-

pictures-social-media

Twitter handles

MPSBarkDag MPSBarnet MPSBexley MPSBrent MPSBromley MPSCamden metpoliceuk MPSWestminster MPSCroydon EalingMPS MPSEnfield MPSGreenwich MPSHackney MPSHammFulMPSHaringey MPSHarrow MPSHavering MPSHillingdon MPSHounslow MPSIslington MPSKenChel MPSKingston LambethMPS MPSLewisham MPSMerton MPSNewham MPSRedbridge MPSRichmond MPSSouthwark MPSSutton MPSTowerHam MPSWForest MPSWandsworth

Plus:@MetPoliceEvents (Updates from the Met Police regarding demonstrations & events in London)@MPSOnTheStreet (An official MPS account giving an officer on the ground's view of events, operations and other policing activities in London)@MPSDoI (Updates from the Metropolitan Police Service, Directorate of Information)

Police tweets

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

Datajournalismhandbook.org

Reference point 1

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

Data challenge 1: Know your API

See: https://dev.twitter.com/start

1% random sample of the firehose

If not rate limited – all data may be collected

FIREHOSE

Data challenge 2: API bias?

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-Bailón et al, 2012)

Data challenge 3: rate limiting + 1%

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)

Data challenge 4: relation to firehose?

‘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)

Data challenge 5: relation to ‘general public’?

Data challenge 6: what data to collect?

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)

Data challenge 6: how to collect the data?

TWITTER TOOLS

Recent explosion in Twitter tools

• Twitonomy

• Scraperwiki

• TAGS

• DMI Twitter Capture and Analysis Toolset

• MozDeh (and Webometric Analyst)

• NViVO 10

• YourTwapperKeeper

Twitonomy (REST + search API)

Scraperwiki

#horsemeat still producing data in June!

Tweet mapping: geolocations

TAGS

Collects up to 8000 tweets based on hashtags/keywords/users

DMI Twitter Capture and Analysis Toolset

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.

MOZDEH (and Webometric Analyst)

NViVO 10

YourTwapperKeeper

Data challenge 7: how to analyse the data?

What to do about all those bots?

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)

Data collected + methods used

produce specific research object

Where do images fit in the era of ‘Big Data’?

Data challenge 8: representing your data?

Data visualisations: what are they and what do they want?

Data challenge 9: how to deal with ethics?

Data challenge 10: user requirements?

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?

@flygirltwo

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-Bailón, 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.

Links to all mentioned tools

• Twitonomy - http://www.twitonomy.com/• Scraperwiki - https://beta.scraperwiki.com/• TAGS - http://mashe.hawksey.info/2013/02/twitter-archive-tagsv5/• DMI Twitter Capture and Analysis Toolset -

https://wiki.digitalmethods.net/Dmi/ToolDmiTcat• MozDeh (and Webometric Analyst) - http://mozdeh.wlv.ac.uk/ +

http://lexiurl.wlv.ac.uk/• NViVO 10 - http://www.qsrinternational.com/products_nvivo.aspx• YourTwapperKeeper -

https://github.com/540co/yourTwapperKeeper

See also: http://mappingonlinepublics.net/tag/yourtwapperkeeper/