1.
Twitter, interpretation
and math
New interdisciplinary approaches to large-scale analysis
of the digital media landscape
Mattias Östmar,
independent data scientist

2.
Problem 1: Access
• New media data is locked-in by commercial
companies
• E.g. audience studies of news diffusion, digital
antropological studies of cultural expression.
• High costs for buying media data from e.g. GNIP

3.
Problem 2: Coding skills
• The data volumes requires technical data-analysis
skills
• E.g. quantitative content analysis of the topics of
millions of news media links shared and
discussed on Twitter.
• Natural Language Processing, Information
Retrieval, API-usage

4.
Problem 3: Collaboration
• True interdisciplinary research groups are still ad-
hoc at best
• E.g. social science, computer science and
theology studying the media logics of kindness.
• Technical infrastructure and competence is
scattered geographically and across institutions

5.
Why I am doing this
• How do ideas spread?
• How do we construct identity with digital tools?
• Can we measure the “social health” of society?

6.
The proposal
• I donate a dataset of 600+ million Swedish
(scrubbed) tweets from 450+ thousand
(anonymized) users
• Seek long-term funding for technical, administrative
hosting in an collaborative academic setting by
initiators at Södertörn University, possibly together
with KTH.

7.
Methodologies and
research areas.
Examples.

8.
Twittercensus
by Hampus Brynolf

9.
Another perspective …

10.
Another perspective …
1 899 739 mutual @-mentions:
@user1:
Have you seen this
@user2? [link]
@user2:
@user1 No, thanks!

11.
Another perspective …
1 899 739 mutual @-mentions:
@user1:
Have you seen this
@user2? [link]
@user2:
@user1 No, thanks!
user2
user1

12.
Network Theory
@user1:
Have you seen this
@user2? [link]
@user2:
@user1 No, thanks!
user2
user1

13.
Amateur research in writing
style in social media

14.
Graph theory gives a mathematical basis
for choice of (qualitative) categories

15.
Large-scale text analysis
using Python
http://localhost:8889/notebooks/swe_hashtags.ipynb#

16.
Large-scale text analysis
without coding skills
http://localhost:5601/#/dashboard/Mindalyzer-Dashboard

17.
Ethics: challenges
• Twitter user privacy, deleted tweets, opinions etc
• Twitter terms of service, API rate limits etc
• Research ethics, types of questions studies etc

18.
Ethics: A starting point
• Distant reading by default (structure over meaning)
• Individuals can donate API access to research
• Improve academic institutions data ethics policies!

19.
Scrambled text format

20.
Media diffusion data

21.
Let’s do text analysis for
social good!
Mattias Östmar
twitter.com/mattiasostmar
www.mattiasostmar.net