Meaning-making, rhetoric and boundary work on Twitter
(ADD YR NAME AND AFFILIATION -- IN ALPHABETICAL ORDER PLEASE)
- Anita Say Chan (University of Illinois, Urbana-Champaign)
- Sofia Chiarini (Density Design Team)
- Katie Clarke (Universitait van Amsterdam)
- Alexandra Deem (Universitait van Amsterdam)
- Lisa Dekker (Universiteit van Amsterdam)
- Asbjoern Fleinert Mathiasen (Aalborg University, CPH)
- Chiara Milan (Scuola Normale Superiore & Georgetown University)
- Stefania Milan (Universitait van Amsterdam)
- Victoria Siegismund (Goethe University Frankfurt)
- Katrin Tiidenberg
- Steve van Velzen (Universiteit van Amsterdam)
- Floor Zijp (Universiteit van Amsterdam)
Summary of Key Findings
Briefly describe your most significant findings.
Introduce the subject matter, and why it is compelling / significant.
#metoo is a hashtag that gained incredible momentum across social media platforms in October 2017. It functions as an ad-hoc hashtag public for highlighting sexual harassment and assault of women. While the phrase and its link to issues of sexual misconduct is more than 10 years old, it was brought into the limelight by Hollywood actresses responding to the harassment allegations against Harvey Weinstein. The hashtag quickly spread through Twitter, Facebook and Instagram, where both famous and non-famous women added their voice and their experience to the public discourse. It has also been extensively commented on by journalistic media.
The speed and global spread of the hashtag, especially for a gendered, feminist initiative, is truly remarkable. By early November it had been tweeted more than 2.3 million times from 85 countries, and on Facebook more than 24 million people had participated in the hashtag public by creating over 77 million posts, comments or reactions. The heaviest tweeters were people in the USA, UK, India, France and Canada. The hashtag has also been discursively productive - it generated a variety of offshoots ranging from #metoo in different languages (#yotambien, #balancetonporc, #quellavoltache, وأنا_كمان# and وانا_ايضا#) to #HowIWillChange and #IveDoneThat, which were meant to offer men a way to participate in the discussion and take responsibility, but was seen by some in the #metoo public as an attempt to mansplain, find excuses or just steal the attention from #metoo. In American press, celebrities and experts commenting on #metoo repetitively use phrases like “watershed moment” and “cultural shift.” Finally, on December 6th Time Magazine chose members of #metoo as their person of the year, calling them Silence Breakers.
The purpose of this project is to explore the discourse of #metoo on Twitter, with a specific interest in what is being enacted and constructed in the these tweets, what it can tell us about (re)configurations in the rhetoric about victimhood, gender, blame, assault, consent etc.
2. Initial Data Sets
List and describe your data sets.
The data set on http://tcat12.digitalmethods.net/analysis/index.php?dataset=metoo
contains 2.569.369 tweets by 1.092.278 distinct users and ranges from 2017-10-18 (the story broke on 5 October 2017, the first tweet with #metoo was sent on 15 October) until 2018-01-02.
For a timeline of events see e.g. http://www.chicagotribune.com/lifestyles/ct-me-too-timeline-20171208-htmlstory.html
3. Research Questions
Clearly state your research questions and any hypotheses / expectations.
Explain your methodology / approach.
Illustration in a TCAT tutorial/walkthrough.
We thus looked at
- Who steers the meaning-making within the the #metoo hashtag?
- Who are the more popular #metoo tweeters?
- Who steer the contra-discourse?
- Analysis of #metoo tweeters (location, number of followers, but also gender and race, might be something that needs to be manually coded based on profile images)
- Rhetorical analysis of
- visual discourse analysis of any added or linked images and memes
- how to extract the most prominent #metoo tweeters;
- how to extract the most prominent mentioned tweeters;
- how to extract the most prominent hashtags;
- how to make a subset based on data i.c.w. a (set of) hashtag(s), tweeter(s) or mentioned tweeter(s);
- how to make a co-hashtag or mention network;
- how to export details of #metoo tweeters;
- how to export and download linked images and memes;
- how to make a full export of all tweets and their meta-data in a (sub-)set for qualitative analysis.
Tool tutorials on working with TCAT and Gephi can be found on https://www.youtube.com/watch?v=_h2B2CA-btY&list=PLKzQwIKtJvv9TT48wkjeW27XhIpJqgRcn
Describe your findings. Consider any counter-intuitive findings.
Discuss and interpret the implications of your findings and make recommendations for future research and application, be it societal, academic or technical (or some combination).
Present a summary of what you have found, and its significance.
List your references in a standard academic bibliographic format.