#MeToo Anti-NetworkInformation about the currently selected diagram.

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About

#MeToo Anti-Network is a project by Kim Albrecht with textual and conceptual support from Catherine D'Ignazio, Cole Martin, and Matthew Battles. Supported by metaLAB (at) Harvard, Schlesinger Library, and the Harvard Data Science Initiative.


From a random selection of one million #MeToo tweets, we read through all examples with more than 100 retweets. Only 8 out of the 894 tweets are actual tweets about sexual assault or experiences around the topic of #MeToo. Of the rest, the vast majority are news media posts and political (trolling) discussions, most of them neglecting the specific issues and survivor voices at the heart of the MeToo movement.

The measurements of "success" in networked social media do not render visible the actual importance of #MeToo or the broader phenomenon of structural sexual violence. In crucial ways, the structure of #MeToo is not a network. If we insist on mapping #MeToo as a network – the all-encompassing symbol of digital society – we risk missing fundamental elements of the movement that don't answer to the network analogy. The arbitrary and heavily mediated frame of the 280-character tweet, and Twitter's "rich- get- richer" network effects, amplify some voices at the expense of others. Mainstream white media are central while ethnic media are minor. White women celebrities are central while Black and Indigenous advocates are marginalized. Since Tarana Burke founded the movement in 2006, its goal has been to break the silence around sexual violence and uplift the voices of survivors. But what happens when the platform works against hearing those voices?

Cosmologists say that most of the universe is structured by antimatter. We postulate that social media is similarly structured by effects of the unobserved discourse and experience. The backbone of a movement such as #MeToo is not based on the most-liked and most-retweeted, but by the masses of unobserved tweets. Vast numbers of #MeToo tweets that had no retweets and no likes nonetheless constituted acts of quiet testimony or unassuming solidarity. Conventional measures of network science thus fail to capture the true relevance of #MeToo. As Black feminist Patricia Hill Collins says, "Most activism is brought about by ordinary people like ourselves."

From a distance, the graphics appear as abstract diagrams, similar to Bridget Riley’s work. The beauty of each line contains a powerful request for a reordering of power within society. We present an opportunity to engage with each request—from individual people at individual moments within a collective movement that is not over. #MeToo is urgent, #InvisibleNoMore is urgent, #BelieveBlackWomen is urgent, #MMIWG2S is urgent, #SayHerName is urgent. We are still living in a crisis of sexual violence. So we invite you to ditch the networked metrics and listen. NOTE: If you want to support Black- and Indigenous-led groups doing visionary work on this issue, please donate to the Me Too Movement, Indigenous Women Rising, or the African American Policy Forum.

At 10:21 PM on October 15, 2017, American actress Alyssa Milano tweeted about #MeToo. Some call this the beginning of the #MeToo movement. But the hashtag, and the movement, have a much longer history. In 2006, American activist Tarana Burke used the phrase "Me Too" to raise awareness of women who had experienced sexual assault. The Schlesinger Library tweet collection starts from midnight, October 15, 2017. Thus it covers 22 hours and 21 minutes of tweeting prior to the tweet from Milano. The diagram displays the build-up to virality, while emphasizing the neglected and forgotten ancestors of Milanos tweet. Within the flow of anonymized tweets, some are from Tarana Burke, the activist who started the Me Too Movement in 2006. The database contains eight tweets she posted in the hours prior to Milano’s viral tweet.
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Radcliffe’s Schlesinger Library, the nation’s leading archive devoted to the history of American women, has built a massive digital archive of the #metoo movement. The Schlesinger's collection of tweets begins in 2017, when the hashtag exploded in popularity across the Internet. The growing collection currently contains over 32 million tweets referencing #metoo and dozens of related hashtags.
For qualitative analysis, our research focused on a random sample of one million tweets in the timespan from 2017 to 2020.
One basic probe was to observe the number of retweets of the million tweets. Across the collection, there is a vast discrepancy in the number of retweets, with only a handful of tweets highly shared.
In total, only 892 tweets have more than 100 retweets. In the diagram here, the size of the circle corresponds to the number of retweets.
We reviewed the entire body of tweets to develop a sense of what is shared on Twitter about #MeToo.
A vast number of tweets violate the Twitter Rules. For example, within our probe 153 tweets by @AmyMek have been withheld based on local law(s). Over a fifth of the tweets within the probe consist of doubtful veracity content
Similarly, 28 tweets are temporarily unavailable: Twitter closed the accounts, presumably in response to various violations of the company's terms of service.
Many tweets are clearly produced by bots. Of tweets by humans, a vast number focused on politics. Highlighted in the diagram here, for example, are all the tweets containing the term 'Trump', 'GOP' or 'Kavanaugh'.
The tweet with the most retweets, is a tweet in Thai. While this tweet received 37,500 retweets, it 'only' has a favorite count of 2848. This kind of discrepancy between likes and retweets, often referred to as "ratio," suggests bot-boosted activity.
The more time one spends with the most popular #MeToo tweets, the more problematic the dataset becomes. Observing these aspects of highly-trafficked tweets, questions arises: what is #MeToo actually about? How do social media economies of attention distort the experiences at the root of the movement?
The #MeToo movement has been understood as a social movement against sexual harassment and violence, activated by people making public allegations. But within the most shared tweets, only nine wrote about personal experiences of abuse or harm.
After undertaking a conventional network analysis, we realized that network measures did not surface the most important voices in the #MeToo movement. Between the movement's goals and the networked effects of social media metric mysticism, there lies a discrepancy. Thus, our #MeToo Anti-Network analysis dismisses network measures of "success". Our research offers a qualitatively–curated collection of frequently unheard tweets that were lost in the networked effects of social media. What we offer here is an aesthetic invitation to re-center the movement: to experience individual stories as evidence of the structural problems of mediated social media movements and the larger society that produces them.