Men’s voices are clearly present in the #metoo conversation. One finds headlines arguing that "Exemplary" Men Should Be Leading The #MeToo Discussion” and that they should speak louder, as is the case with hollywood actors. Men are using social media to take accountability and raise awareness, while elsewhere they are being framed as victims of the movement, now described as a witch hunt. Moreover, there is both a foregrounding of the importance of including male victims — “Me Too: The Difficult Truths About Gay Men And Sexual Assault”-- as well as a questioning of the of pertinence their stories: “What Happens When Men Say #MeToo, Too?” In all, the role that male voices play in this ongoing conversation begs further exploration from a wide range of perspectives including social movements, feminism, and new media studies. Thinking about the men of #metoo beyond the position of perpetrators of abuse, in this research we ask: what roles are men and their voices playing in the movement? How do they shape it and which positions is social media enabling them to assume? And how may such questions be operationalized as a Twitter analysis using digital methods?
We take these questions to a dataset of about 2 million tweets collected using the hashtag #metoo, during the period of 18 October 2017- 2 January 2018, amounting to 11 weeks. Following the medium, so to speak, we take as starting point of the research hashtags, which in social media help organize thematic streams. We ask: are men’s voices organizing thematic streams? Using the TCAT we extracted the most frequently hashtags used in combination with #metoo and that relate to the issue of men in movement. We created a hashtag “dictionary” in which hashtags are organized according to how they enable men be brought into or participate in the movement. As expected hashtags that ‘call out’ abusers are the most prominent but also one finds hashtags that enable men to support the program, for example by acknowledging women’s testimonies or by presenting an anti-program.
From the ‘dictionary’ we choose to study in more detail the following hashtags: #howiwillchange, #Ihearyou, #notme, #sheknew, and #himtoo. The abovementioned hashtags are not only relevant in side the dataset (in terms of frequency) but they have also been in the news and have been recognized as affecting the conversation in number of ways. For example, #himtoo is employed by twitter users both to denunce abusers but also to expose the abuse committed to men who are victims of sexual assault. The hashtag #howiwillchange enables forms of accountability, for example, “in Response To #MeToo, Men Are Tweeting #HowIWillChange”. Similarly, by using I #Ihearyou: “Men are stepping up with #IHearYou in response to women’s”. Lastly #notme functions an anti-program: “In this instance, #NotMe is to #MeToo what #AllLivesMatter is to #BlackLivesMatter”.
We set to test these claims further and explore the spaces created around and by these selected hashtags. We created ego and co-hashtag networks for each of the hashtags and then explored the sub-issues, actors, discourses, and positions that they enable and it changed over time. Afterwards we focused on retweets: which men are retweeted the more? Lastly we returned to the issue of mainstream media and asked, how are men related voices and issues supported, up-taken by mainstream media and circulated in twitter?
Through the analysis of the hashtags that were more frequently employed in combination with #metoo over the period of 11 weeks, we may conclude that men voices are present and currently playing a significant part in the thematic distribution of the dataset.
For the visualization below the top 20 are kept and the five hashtags related to the voice of men are highlighted.
What sub-issues (co-hashtags), actors (mentions and users), and discourses come together around this five hashtags? How has this change as the movement evolves? And as the initial examples of headlines suggest, how are this men related voices supported, up-taken by mainstream media.
Co-hashtag over time identifying those hashtags over time.
1 #howiwillchange, #Ihearyou, #notme, #sheknew, #himtoo
3 #howiwillchange, #Ihearyou, #notme, #sheknew, #himtoo
9 #howiwillchange, #Ihearyou, #notme, #sheknew, #himtoo
Evolution of #howiwillchange during 11 weeks;
So you have a lot of tweets that you have captured with Tcat and now you want to do something with them. One way of interpreting your captured data might be through a network visualisation in Gephi. Here's how we got to a nice spatialised network:
Query Tcat for thematic hashtag that represent parts of the overall #metoo hashtag-space one by one. (#howiwillchange, #Ihearyou, #notme, #sheknew, #himtoo)
Export Co-hashtag graph from the Tcat interface using minimum frequency of 2.
Open the graph file in Gephi.
Delete the #metoo and the thematic hashtag that you queried in the data laboratory.
Spatialise the graph, under layout, using the Force Atlas 2 algorithm. (Change scaling by temperament (usually in the 40’ies))
Choose prevent overlap AFTER the network is properly spatialised
Apply Giant Component filter to weed out unconnected nodes (eg. nodes that have a frequency of less than 2 and are thus not connected) (Omitted giant component on #ihearyou network due to topology)
Rank the nodes according to degree (Min size 10. Max size 40)
Run average degree statistics
Run unweighted modularity statistics (Omitted on the #ihearyou network due to the topology of the graph)
Colour nodes according to modularity class (Omitted on the #ihearyou network due to the topology of the graph)
Run Nooverlap layout algorithm IF labels are unreadable because of overlap
Rinse and repeat for the specific queries.
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Cluster if yes: describe them
Query TCAt with hashtag in interesting clusters:
Most interesting looking
In the first week of our dataset (18-24 October 2017), McKayla Maroney is the Twitter user that is mentioned the most by tweets containing the metoo hashtag. Maroney is mentioned a total of 19327 times in this week. This is the highest mention number for a user for the whole dataset in one week. Maroney is a famous United States gold medal winning gymnast that accused her team doctor, Larry Nassar, of sexual assault in a twitter post on the 18th of October 2017. She hashtagged the post with #metoo. Larry Nassar is now accused by many more gymnastics and is sentenced to 60 years in prison for possession of child pornography. We see over the next weeks that famous women, actresses, journalists, authors etc, are at the top of the list of most mentioned users in relation to #metoo. Alyssa Milano, the actress who started the hashtag movement, shows up in the top three of most mentioned users in three of the eleven weeks that span the dataset. US (media) companies are also popular, as for example Huffington Post, TIME, Unicef USA and NPR are in the top three over the course of the eleven weeks.
What we see in week five of our dataset (8-14 November 2017) however, are the first male twitter users showing up in the top three. Two users, @CernoCreatives and @Anti1802 are not famous people but push a clear anti Hollywood agenda, using the metoo hashtag to point out the hypocrisy within this community. It seems the hashtag is here not being used to ally or protest against the ‘movement’, but to serve a broader goal that is somewhat disconnected from sexual assault. The criticism seems to be directed at stardom and the disbelieve in the political system. Week nine of the database (13-19 of December 2017) has a political top three of most popular users in relation to #metoo. With US Senator Kirsten Gillibrand (Democrat) at the top. Second to that is Scott Dworking from the Democratic Coalition Against Trump. Dworking clearly uses the metoo hashtag as an attack to Donald Trump. Trump is number three at the top for this week’s most popular mentioned users in relation to metoo. We see an enormous spike in the use of the word ‘Trump’ in context with the metoo hashtag too around this time (see figure 1 on next page). Most probably because CNN posted and aired a story on the 12th of December of three women coming forward accusing Trump of sexual harassment. We see a clear shift here from famous women sharing their metoo stories to a political context. Week ten of the dataset shows a shift to Japanese users, in particular at the top user @biancajyojyoen who got mentioned 8424 times in relation to metoo. This Japanese woman tweeted she was a victim of workplace sexual harassment, and although she relatively only has 2681 followers (at this moment), her tweet (in Japanese) was retweeted 15,437 times. Week eleven and the last week of our dataset (27th of December 2017 until 2nd of January 2018) has @movietvtechgeek at the top as most mentioned, an account with the description “Entertainment news for the masses”. Indeed this account tweets news stories and developments in the movement, and does not ally or protest per se. The second most mentioned user however is Katie Hopkins (@KTHopkins) a white christian author that is known for her anti political correctness. She is the only person found in this top mentioned data that is clearly against the movement. Reading through her tweets it becomes clear she is anti-muslim and also anti-gay in particular. In relation to #metoo she thinks women should “man the f*ck up” and “quit with the #metoo cr*ap”. Al tweets relating to #metoo have now been deleted from her account.
Men who are most retweeted: How voice most heard and is this changing ?
In five of the 11 weeks analyzed, the most frequent URL tweeted was one of someone in the public eye joining the #MeToo movement or being accused of sexual harassment: Monica Lewinsky tweets #MeToo; former Crystal Castles band member Alice Glass accuses band mate Ethan Kath of abuse; basketball star Breanna Stewart writes a blog post about being abused as a child; Leann Tweeden accuses Al Franken of sexual harassment; Melissa Schuman accuses Nick Carter of sexual harassment. The top URLs in other weeks include a Dylan Farrow op-ed questioning why director Woody Allen has not been hit by the #MeToo movement, calls for a Congressional investigation into the sexual misconduct allegations against Donald Trump, or how this movement made history. This pattern would suggest that #MeToo kept going thanks to new scandals involving people in the public eye.
The above graph shows the website host frequency mentioned with the #MeToo by week, split between some right and left wing sources. It becomes clear that mainstream, left-leaning news sources such as The New York Times, Huffington Post, Washington Post and The Guardian, had a constant, big presence in the conversation around #MeToo. On the other hand, right wing sources Fox News, Breitbart and Info Wars barely appear.
When we look at the left-wing sources that had a big presence in the debate. First, Huffington Post played a big role in shared URL’s on Twitter. In the list of most shared URL’s on Twitter Huffington a lot of them are Japanese articles. This is quite interesting, especially because other mainstream (Western) media have not reported about this so much. Also this is a point where the #metoo discussion crosses the European and American borders and spreads to Japan. Furthermore, many articles are about US politics, naming senators and president Trump. Second, the articles of the New York Times cover a lot of news about celebrities. They also talk about ‘the me-too moment’ in sports. There’s a lot of pro me-too in opinion articles and personal stories from for example Salma Hayek. The Guardian looks more at the male side of the story. They state in the headlines that men should speak up. They are clearly stating that the accused men are no victim and should think about what it’s like for women. Last, the Washington Post discusses both sports and tech where there are the so called #metoo-moments. It’s striking that in most headlines the Washington Post literally uses the term #MeToo.
On the other side in the more right-winged department, Fox News’ big presence in week one, that week’s top URL, is due to one article about Monica Lewinsky tweeting #MeToo. When looking closer at the articles that appear from these three sources, one noteworthy observation is the lack of any mention of former Republican senator Roy Moore and the allegations against him leading up to the Alabama senate race. At the same time, Fox News and Breitbart each have an article related to the allegations against Democratic senator Al Franken, showing that they will write about this issue when it is about someone on the other end of the political spectrum. They also don’t miss out on the opportunity to attack other news outlets: in a Fox News article about a 2008 secret ‘roast’ where media executives and personalities (mostly from NBC) allegedly joked about Matt Lauer’s inappropriate workplace behaviour and infidelity, Fox uses harsh language to suggest that people at the network knew about Lauer, even though some of them publicly declared their shock once the allegations against him were made public and he was fired.
The allegations against Donald Trump are barely acknowledged by right-wing media, and when they are, they are not treated in a serious manner. An article from Info Wars actually suggest a conspiracy against the US president, as the activist group that hosted a press conference for the women who accused Trump of sexual misconduct was allegedly funded by billionaire George Soros. Also from Info Wars, there is a video discussing how feminists “stole” #MeToo, where the female host calls the allegations against him “minor occurrences” of what one might call sexual harassment, essentially dismissing them, while questioning why no one is talking about the women who accused Bill Clinton of rape, a much more serious manner. This turns it into a partisan issue: supporting the Republicans, accusing the Democrats.
The previously mentioned Info Wars video, titled “Why Feminists Stole #MeToo”, is mainly focused on Time’s article naming the #MeToo movement the person of the year, and the host herself makes the issue political, arguing that Time framed the article against Trump and calling it “a hit piece on their [Time] political opponents, Donald Trump, Roy Moore, and the Republican party”. The host goes as far as calling Ashley Judd, one of the cover stars and a prominent figure in the movement, a ‘nasty woman’ who is against Trump, and questions whether the reason Terry Crews was the only male victim mentioned in the article is because he “ticks the minority box” as an African American. The main point of the video is that Time ignored the male victims in the #MeToo conversation, giving statistics in order to prove that men are abused more often and more seriously than women but stay silent more often. The host believes that the “overreporting” on harassment against women is happening in order to skew what sexual assault means, because the left wants women to dominate and subjugate men.
While other articles have some reasonable points, the political and ideological differences still bleed through. For example, an opinion piece from Fox News has a strong sense of irony, as the author points out the hypocrisy of the Hollywood elites who claims to support women’s rights only to be accused of harassment, as this comes from a writer and outlet that support Donald Trump and Roy Moore.
Overall, there seems to be a clear difference between how right and left wing media has been reporting this issue. Left wing sources discuss a variety of issues and facets of the movement, and don’t shy away from disavowing the men accused, even if they are on the same side of the political spectrum. They are also women’s go-to news sources for telling their stories. Meanwhile, the right, while reporting on various issues and men accused, refuse to acknowledge right wing politicians’ involvement and accusations against them, or if they do, they are dismissing or overlooking them; and they don’t shy away from attacking liberals and their competition. They are the side using this movement politically.
CNN appears to be the most constant retweeted news source over time.
In the four most linked to newspapers, the male viewpoint is mostly being ignored. Only one article in The New York Times mentioned #HimToo . But this article depicts not a male viewpoint in the #metoo-discussion, but is rather a collection of stories about Republican politicians who are being accused of sexual misconducts. #IHearYou is only being mentioned once in all of the 1151 news stories on #MeToo. This is an article, written by a male journalist, about how men should conduct themselves after the #MeToo stories. #HowIWillChange is not mentioned in the dataset.
This dataset consists of all the 1151 news stories about #MeToo in the four newspapers that are being mentioned the most on Twitter in relation to #MeToo. That would be The New York Times, The Guardian, The Washington Post and The International Herald Tribune (as a supplement to The New York Times).
The four newspapers didn’t write stories about male victims of sexual misconducts. If a news story broke where a man accuses another man, the articles focus on the accused rather than on the accuser. You see this happening with the Kevin Spacey case. When movie star Spacey is being accused of sexual misconducts, it instantly becomes the most important story in these newspapers. But the fact that he is being accused bij Anthony Rapp is mostly being ignored. Only The Guardian wrote one article about the abuse of boys in Hollywood, where the case of Anthony Rapp and Kevin Spacey is mentioned in.
The most used words in the four newspapers in relation to #MeToo:
Over the past few years, software robots – often abbreviated as bots – have gradually inhabited digital social networking sites, increasingly adapting to online human behavior and thus making it ever more difficult to discern them from legitimate (human) users (Ferrara et al. 2016). Today, these sophisticated social bots are not only capable of automatically (re-) producing content but also of interacting with other users. By circulating high volumes of information, bots play an exceedingly relevant role in shaping online information spaces and influencing users’ opinion. This became particularly evident in the 2016 U.S. Presidential elections, during which the targeted deployment of local and foreign bots came to light (Bessi and Ferrara 2016). For that reason, we decided to take a closer look at the bots that dominate the #MeToo discourse on Twitter, detecting possible changes in their presence over the course of twelve weeks (18/10/17 – 09/01/18), thereby allowing us to assess the overall health and authenticity of the campaign.
Using the DMI-TCAT, we initially extracted the most active users in terms of tweet frequency for each of the analyzed weeks, focusing only on the first ten ranks. The activity of the most active individual accounts ranged between 2,078 posts in week 8 and 215 tweets in week 12. In order to detect social bots, we examined each of the 80 different accounts by applying a three-step method. We first entered each user name into the Botometer, a tool developed by the Indiana University Network Science Institute (IUNI) and the Center for Complex Networks and Systems, which calculates the likelihood of a user being a bot by relying on a complex classification algorithm (IUNI 2018). While this allowed for a high probability of discerning bots from humans, it could not be ruled out that some bots have gone unnoticed. We therefore verified each individual account manually on Twitter, focusing first and foremost on the ratio between the overall number of posts that have been sent by the user and the days active. According to the Oxford Internet Institute, an average of more than 50 tweets per day can be regarded as highly suspicious (Neudert et al. 2017: 4). In addition, the users’ profile picture and personal details were taken into account, viewing a high degree of anonymity as another possible indicator (Stukal et al. 2017). In a third step, we extracted each user’s tweets containing the hashtag #MeToo in order to detect structurally repetitive tweet behavior, i.e. a procession of retweets or long strings of identical posts or shares, especially news stories or advertisements. By applying this three-step approach on the ten most active accounts for each week, we identified 28 individual accounts (35%) that have a high probability of being bots and 52 accounts (65%) that are likely operated by humans.
As might be expected, bot accounts not only continuously occupy the first ranks of the most active users, but their presence also increases over the course of the 12 weeks, gradually supplanting human users and thereby hijacking the #MeToo discussion week by week: While in the first six weeks, only three to five bots dominate the discussion, this development culminates in week 11 (27/12/17 – 02/01/18), in which eight bots have captured the hashtag #MeToo. Prima facie, this seems to not bode well for the overall health and authenticity of the campaign. But the prevalence of bots in terms of tweet frequency required us to analyze further, raising the questions: What types of bots are they? Which agenda, political or otherwise, do they try to pursue? Do they support the #MeToo campaign or do they rather seek to undermine it?
By qualitatively examining the individual accounts as well as their posts containing the hashtag #MeToo, we identified six different categories of bots (see Figure 1). As anticipated, the discussion is firstly dominated by 15 different spam or advertisement bots – many of which are connected in a so-called botnet, tweeting identical content at the same time or retweeting each other – that merely exploit the trending hashtag for promotional reasons or post poems or quotes that exhibit no substantial connection to the #MeToo campaign whatsoever. Contrary to our expectations, however, almost as prominently represented are bots that explicitly endorse the #MeToo campaign and can therefore be interpreted as quasi-feminist bots, which shall be further differentiated below.
Japanese feminist bots (e.g. @chuta21karass, @key_akemi1225, @ 985755Tyaina, @bQ1r9Gxdq7AebV6) play a key role in promoting the initiative’s empowering message at a high frequency, especially by demonstrating support with a Tokyo-based journalist, Shiori Ito, who accused a well-renowned journalist and biographer of Japanese Prime Minister Shinzo Abe of sexually abusing her while she was unconscious (Rich 2017). These bots primarily (re-) tweet news stories and interviews with Ms. Ito, often underlined by hashtags such as #FightTogetherWithShiori, #JusticeForShiori or #TheSilenceBreakers. After the Japanese discussion – predominantly lead by legitimate users – reaches a peak in week 10, the discourse is taken over in the subsequent week by Japanese bots, which largely uphold the supportive stance toward the #MeToo campaign, despite being joined by Japan-based spam and advertisement bots (see Figure 2). The Japanese case is all the more remarkable, considering the fact that “sexual assault remains a subject to be avoided in Japan” (Rich 2017). Thus, the bots play a significant role in drawing public attention to Ms. Ito’s case in particular and sexual misconduct in Japanese society in general.
Figure 1: Categories of bots dominating the #MeToo debate on Twitter – week 1 to 6.
By far the most active and persistent account (@YouAreOnRadar) is a bot located in New Delhi, that claims to endorse a project called ‘Abuse Free India – Campaign against abusers on Twitter’. This bot exclusively reports Tweets with misogynistic content by publicly shaming their originator, either claiming “You have been caught abusing on Twitter.” or “This person is a serial online abuser. (…) Report Tweet”. Although the agenda behind this bot is hard to grasp, its strategy of public humiliation – a method that is often employed by Indian public authorities (Doshi 2017) – can be interpreted as a drastic, if arguably ineffective measure to publicly denounce and socially punish sexist behavior on Twitter.
An outlier within the supportive bots is a Germany-based account named @TrumpernTolshek (see Figure 2), that pursues a particularly radical feminist agenda, sending over 600 posts in the ninth week of the analysis period that contain the message: “FIGHT ON! >MEN are unfit to lead the WORLD to PEACE & CIVILITY, are PREDATORS exploiting MILLIONS of WOMEN & GIRLS (…)”. This is often followed by the hashtag #MaleRottenApples, possibly alluding to a feminist online database listing movies and TV shows associated with directors or producers who have been suspected of sexual misconduct (Salam 2017). While the radical feminist standpoint seems undeniable, the political background of this specific bot as well as the genuineness of its support for #MeToo remain unclear. Consequently, it cannot be ruled out that this bot is in fact a troll, trying to undermine the #MeToo campaign by sowing discord and intending to provoke strong and emotional counter-reactions against the campaign.
Figure 2: Categories of bots dominating the #MeToo debate on Twitter – week 7 to 12.
At the same time, overtly anti-feminist bots are in a distinct minority amongst the most active users, only one could be identified for the whole of the analysis period: 151 posts of @Shankarrao1753 (week 3) are filled with hatred against women, illustrated by hashtags such as #FeminismIsCancer, #FalseRapeCases and #teachyourdaughtersnotorape. This Mumbai-based bot also directly addresses the Indian Prime Minister Narendra Modi with news stories about women accused of murder or false testimony, thereby aiming at publicly and politically discrediting the #MeToo campaign and feminism in general. The now deleted, overtly xenophobic account @sninfoflux attempted to frame the discussion about sexual abuse and violence as an “Islamic problem”, using the hashtag #NotOurMen to blame Muslim men as the alleged main perpetrators.
Over the course of the 12 weeks, the bots also become more distinctly politicized, with automated accounts connecting the #MeToo debate to US President Donald Trump, who himself has been accused of sexual misconduct in the past (Shear 2017). In week 4, which marks the first anniversary of Trump’s election, the most active automated user, @Ajain31, repeats the allegations against the US President in 359 posts, tweeting “#impeach45 4 Sexual Misconduct”, followed by hashtags such as #TrumpToo and #HoldTrumpAccountable. This particular anti-Trump bot stays among the most active users up to week 12 and is joined by other leftist bots that criticize the current Commander-in-Chief and link the #MeToo campaign to the #BlackLivesMatter movement. Bots supporting and defending Trump, on the other hand, are much less active.
In sum, one might not be surprised by our findings that many (though not all) of the most active accounts using the hashtag #MeToo are in fact software robots and that they seem to gradually hijack the public debate on sexual abuse and assault over the course of the first twelve weeks of the campaign. Nevertheless, instead of simply discarding all of their posts as noise and prejudging the campaign as “bot-infested” and therefore unhealthy, we found it was worth having a closer look at the different categories and agendas, keeping in mind that bots – though merely programmed software – have a crucial and ever-growing impact in shaping and amplifying public opinion. In stark contrast to preceding studies on bots and the general assumption that social media is in the tight grip of promotional, trolling, destructive bots, we found that feminist bots play an important role in portraying the #MeToo campaign in a positive light, supporting victims and drawing attention to the often-tabooed issue of sexual abuse and violence.
Dataset of over 2M tweets with hashtag #metoo, over 11 weeks
The conversation links to the tech industry only after a week (around October 20).
The ‘tech/nology’ scene (?) is evoked in the conversation, although minimally (< 0.5% of the sample) in a variety of ways.
Three main angles: i) technology and #metoo (how users speak about technology); ii) the tech industry (are cases of sexual harassment/violence outed on Twitter in relation to #metoo?) and iii) the digital rights community (is the ongoing controversy within the community connected to #metoo?)
Narratives around technology (co-occurrence of #metoo and [tech_] in the datasets:
Technology (social media) as empowering women to speak up (associated hashtags: #breakthesilence)
But also technology (i.e., platforms, algorithms) as altering the conversation (example of shared content)
Technology magazines/sources are quoted as sources of news on the #metoo topic/conversation
The tech industry is known to be a male-dominated environment. The survey ‘Elephant in the Valley’ (early 2017), involving over 200 women with at least 10y experience in the industry, reported that 60 percent of women in tech have reportedly received unwanted sexual advances.
The #metoo discourse was first explicitly connected with the tech industry by a post by Quinn Norton, a tech journalist, published on Medium on October 19. The post called out on Robert Scoble, tech author and self-defined ‘tech evangelist’, as a sexual offender. The story, and the subsequent public apologies by Scoble, were picked-up by mainstream media like the NY Times and USA Today.
Contrary to what expected, the conversation on Twitter did not develop around specific hashtags, such as #tech or #techindustry or #siliconvalley. A closer look at a few of these thematic hashtags reveals the following:
#tech was used almost exclusively in relation to the Scoble case. Associated hashtags are #womenintech and #womeninleadership (very limited use). No conversation
#siliconvalley has been used almost exclusively in relation to the Scoble case. No evidence of push-back. Associated hashtags are #womenintech (very limited use)
#startup / #startups has also appeared in a small subset of Tweets, often associated with related hashtags such as #entrepreneur and #biz, but spurred hardly any exchange/conversation
#womenintech is only marginally used, and largely by female users.
No pushback from men in tech; not engagement at all!
Possible explanations: as Vanity Fair reported on December 15, “We know now, of course, that this behavior is hardly germane to Silicon Valley, but for some reason, the #MeToo movement hasn’t pierced the tech bubble the same way it has other industries.”
The digital rights community sports its own cases of sexual harassment/violence, which pre-date the emerging of the #metoo conversation. The most prominent cases concern Jacob Appelbaum (Tor), Morgan Marquis-Boire (Citizen Lab et al), Ali Bangi (ASL19); Robert Scoble; the first pre-dates the #metoo hashtag; the Morgan case was brought to the public eye exactly on October 13 and garnered quite some press attention; the third case overrides the #metoo conversation.
One topical moment for the community was the 34th edition of the Chaos Communication Congress, probably the biggest hacker congress in the world (Leipzig, Germany, 27-30 December 2017; related hashtag #34C3). While known perpetrators have not been excluded from participation, related workshops have been prevented from taking place/being included in the program, to the point that a prominent female hacker (and victim) published on December 26 a post entitled “The CCC: Men Who Hate Women”.
Surprisingly, and mirroring closely what we have seen happening within the tech industry, the digital rights community did not appropriate #metoo, although the issues at stake are the same and the discussion unfolded around the same time.
#metoo entered in the 34C3 debate only by means of a) media reports, and b) women who decided to use their spot in the program to mention it (and the tweets that voiced that).
The conversation unfolds on mailing lists and IRC chats
Internal dynamics of the community: by reputation and not by numbers
Bessi, A. & Ferrara, E. (2016). Social bots distort the 2016 U.S. Presidential election online discussion. First Monday, 21 (11).
Doshi, V. (2017). India turns to public shaming to get people to use its 52 million new toilets. The Washington Post, 5 November. Available at: https://www.washingtonpost.com/ world/asia_pacific/india-turns-to-public-shaming-to-get-people-to-use-its-52million-new-toilets/2017/11/03/882166fe-b41c-11e7-9b93-b97043e57a22_story.html?utm_term=.b9e5b155e4aa (accessed 10 January 2018).
Ferrara, E., Varol, O., Davis, C., Menczer, F., and Flammini, A. (2016). The Rise of Social Bots. Communications of the ACM, 59 (7): 96-104.
IUNI (2018). Botometer. An OSoMe project (bot•o•meter). Available at: https://botometer.iuni.iu.edu/#!/ (accessed 10 January 2018).
Neudert, L.-M., Kollanyi, B., and Howard, P.N. (2017). Junk News and Bots during the German Parliamentary Election: What are German Voters Sharing over Twitter? Data Memo 2017.7. Oxford, UK: Project on Computational Propaganda.
Rich, M. (2017). She Broke Japan’s Silence On Rape. The New York Times, 29 December. Available at: https://www.nytimes.com/2017/12/29/world/asia/japan-rape.html (accessed 11 January 2018).
Salam, M. (2017). Website Helps Movie and TV Fans Keep Track of Hollywood’s ‘Rotten Apples’. The New York Times, 13 December. Available at: https://www.nytimes.com/2017/12/13/ business/media/rotten-apples-sexual-misconduct.html (accessed 11 January 2018).
Shear, M.D. (2017). Trump Sexual Misconduct Accusations Repeated by Several Women. The New York Times, 11 December. Available at: https://www.nytimes.com/2017/12/11/us/politics/ trump-accused-sexual-misconduct.html (accessed 13 January 2018).
Stukal, D., Sanovich, S., Bonneau, R., and J. Tucker. (2017). Detecting Bots on Russian Political Twitter. Big Data 5 (4).