Mapping Mobs - Technological affordances, metrics, and digital violence against journalists.

Team Members

Darja Wischerath, Rosalie Dielesen, Danique Leenstra, Bobby Uilen, Zarah Noorani, Luisa Garcia Amaya, Camilla Folena, Sofia Ompolasvili, Ashraf Sahli, Carla Roman, Marloes Geboers, Tomás Dodds, Gen Lemaire, Lotte Timmermans, Dilina Janadith

Contents

1. Introduction

The Dutch ‘Persveilig’ (safe press) organization recently reported 262 cases of aggression and violence against members of the press in the Netherlands, which is double the number of reported cases in 2020 (Persveilig, 2021). In the race for clicks, a human toll is paid. Journalists find themselves in limbo: they are nudged or even pushed to lean into the platform logic of maximizing engagement which might make them more vulnerable to (personal) attacks. Newsrooms take into account audience metrics that are, partly, derived from social platforms. Editorial decisions (Christin, 2020, Petre, 2015), as well as the production of news (Poell & Nieborg, 2018), are more or less affected by journalists ‘leaning into’ what is popular according to audience metrics. At the same time, (digital) violence against journalists is on the rise (Miller, 2021; Waisbord, 2020). Mobs can quickly emerge and ‘impose’ censorship. In this project, we want to study online hostility toward the press through a ‘platform affordances approach’ in which we assume that the architectures of platforms will shape and constrain violence toward journalists in specific ways. Alongside this, we want to assess what issues are prone to ignite mobs and what the role of clickbait and other ‘content styles’ is in relation to mobs going off.

We will not so much focus on regulatory tactics of banning or de-platforming, rather we aim to explore the relationship between engagement intensity (that is, in turn, shaping audience metrics informing journalists) and its predictive value for hostility toward journalists. In the case of the Netherlands, ¾ of cases of violence included personal threats of the journalist, in 29% the news outlet was also attacked, and in 37% of cases the journalistic profession, in general, was a target of aggression. In 3 out of 10 cases violence took place on social media, mainly Twitter and Facebook. Going from this data we aim to zoom in on both who is involved, what societal issues get entangled in the attacks toward journalists, and what the discourses are of the vitriolic messages. For non-Dutch speaking participants: we will widen our net and also collect social media data that ties into the upcoming Chilean elections, as well as English-based cases.

Context

"Elites, norms, and audience. All of these figure centrally in journalism’s sense of self in largely Western or global Northern liberal democracies, and each of them are fatigued. They need to be cast aside and rethought. We argue that the reliance on elites is faulty and that it furthers journalism’s reliance on sectors of the public that aren’t representative. We argue that the reliance on norms isn't helpful because norms are thought to unfold in a perfect aspired condition that isn't at all reflective of what journalists are actually going through. And we argue that the audience isn't there in the way that journalism has typically thought or expected." - Barbie Zelizer on The Journalism Manifesto (2021)

As digital technologies have made their way into news production, allowing news organizations to measure in real-time audience behaviors and engagement, click-based and editorial goals have become increasingly intertwined. Media organizations are now embracing social media platforms to connect and expand their audiences (Lysak et al., 2012). The popularisation of quantitative audience measurements has modified the news-making and distribution process to successfully encompass the new “measured” audience preferences and opinions. In the process of writing for a datafied audience driven by metrics, journalists have stopped writing—according to their values—for a general audience. Instead, reporters write in an attempt to satisfy the algorithm that determines the quantification of such publics (Gallagher, 2017), and therefore for an unperfect datafied audience. Anderson (2011) refers to this phenomenon as ‘algorithmic publics’, a concept that encapsulates the influence of algorithms and its metrics on journalists’ perceptions of the audience, and the influence that those perceptions end up having on the development of news making.

However, while social media platforms have made it possible to connect new algorithmic publics to the newsrooms, they have also played a role in the increase of hostility toward the press in general, and abuse and harassment of journalists in particular. Virtual mobs, at times aided by the algorithm behind these platforms, have found a place to connect online and target reporters across different media organizations (Miller, 2021; Waisbord, 2021).

Online harassment affects journalists globally, but it also presents local and national particularities (Waisbord, 2020). We expand on existing ethnographic research of newsrooms in Chile which focused on how journalists perceive and cope with online vitriol (Dodds, forthcoming). We’d like to build on this and move toward answering questions about the role that platform affordances play. We understand violence very broadly, meaning that we include discourses that undermine trust in ‘mainstream press’ and undermine the public role of independent journalism in democracies.

2. Research Questions

Can we find correlations between resonance in terms of engagement intensities on the one hand and the extent and kinds of hostilities on platforms on the other hand?

Do we see differences between countries and the kinds of hate that are prominent?

What is the role of personal characteristics and perceived political leanings of journalists and news outlets respectively, in the kinds of allegations that are made?

3. Methodology and initial datasets

Data collection and sampling

We queried the Twitter handles of several journalists and news outlets based over three countries: the Netherlands, the UK, and Chili, mostly pertaining to the date range: November 26, 2021, up until January 5, 2022 (with some datasets differing when they would amount to an unmanageable size). This time span includes several events that influence user activity intensities such as the Chilean presidential elections, a lockdown in the Netherlands, and the resignation of a famous female political editor of the BBC, attacked for her ties to Boris Johnson.

country	journalists	outlets
Chile	Mónica Pérez (T13) Carola Urrejola (T13) Consuelo Saavedra (Radio Duna) Juan Manuel Astorga Soledad Onetto Monserrat Álvarez (CHV) Mauricio Bustamente (Cooperativa) Sebastián Esnaola (Cooperativa) Fresia Soltof (CNN Chile) Mirna Schindler (T13) Paula Molinat Daniel Matamala Leslie Ayala (La Tercera)	@latercera @elmostrador @biobio @T13 @CNNChile @Emol @adnradiochile @meganoticiascl @24horasTVN
Netherlands	Maarten Keulemans (Volkskrant, male, perceived left of center) Asha ten Broeke (Volkskrant, female, perceived left of center) Chris Klomp (earlier dataset, due to Twitter break of Chris) Thomas Bollen Diederik de Groot Saskia Belleman	@rtlnieuws @ftm_nl decorrespondent @nunl @ad @telegraaf @volkskrant @nos
UK	Owen Jones Laura Kuenssberg Julia Hartley-Brewer Amelia Gentleman Paul Brand (ITV, uncovered current govt email scandal) Ashley Cowburn (The Independent) Vicki Young (BBC deputy political editor)	@BBC @Channel4 @dailytelegraph @TheGuardian @TheSun @Mailonline

For Facebook data we queried an interesting page that was dedicated to a hashtag that was prominent in the UK dataset: #defundtheBBC, using CrowdTangle . With the dataset coming out of this search, we could see what news sources were shared by this page. We used the CrowdTangle Chrome plugin extension to assess which other pages were sharing the same news articles as this page. See also the Findings section.

We also queried Laura Kuenssberg, a UK journalist to gather posts about her. Through sampling high and low interacted posts, we arrived at a dataset that we used as an entry point for manually and qualitatively analyzing the comments that these posts received. See also the findings section.

Methodologies

Twitter networks

The different datasets were demarcated through customized queries, based on word and hashtag frequencies which in turn were derived from the two statistics modules in TCAT that allow to arrive at such outputs. Underneath we will shortly state the specifics for each country.

-UK data
The hatebase analysis module in 4cat was used to get query words. For comparability, we also included general query words that were frequently present throughout the entire dataset, these were:
journalist OR media OR fake OR scum OR propaganda. In a second step, the entire dataset was demarcated/narrowed down through this hate query that we ran in TCAT.

Using the bipartite hashtag/user network output we could go from TCAT to Gephi where nodes were colored to separate the two types of nodes. Nodes were sized by usage and mention frequency.

-NL data:
Filter out most frequently used words via TCAT (word frequency module).
Make Dutch hate lexicons on the basis of this.
Query dataset in TCAT with hate lexicons. [insert Dutch hate query here]

In a second step, the entire dataset was demarcated/narrowed down through this hate query that we ran in TCAT. Using the bipartite hashtag/user network output we could go from TCAT to Gephi where nodes were colored to separate the two types of nodes. Nodes were sized by usage and mention frequency.

-Chilean data: similar to Dutch protocol.

CrowdTangle, cross-platform analyses and comments analyses

Laura Kuenssberg from BBC is used as a case to systematically compare platforms and their associations with harassment. Below steps were followed in the process.

Twitter data

1. Twitter data collected for the handle of @bbclaurak (2021/11/25 -2021/12/26) - 118,727 items

2. Ran TCAT hostname frequency for the above data set (per day) and identified special peaks.

3. Ran TCAT hashtag frequency for identified journalism/journalist targeted hashtags (fakenews, defundbbc, scummedia, sacklaura) and recorded special peaks.

4. Manually inquired into the potential event/post/news triggered the above 2 and 3 peaks.

Facebook data

1. Crowdtangle: Laura’s name + Twitter handle mention frequency in Facebook

2. Crowdtangle: Interactions for prominent harassment tags based on Twitter data

3. Manually inquired into the potential events/posts/news that triggered the spikes.

A qualitative inquiry into comments

1. 10 most interacted and 10 least interacted articles were selected from data gathered from Crowdtangle for Laura’s name.

2. The comments sections were then thematically inquired to identify hate speech targeted at journalists (professional, personal etc).

3. Keywords used in these comments were then separately listed (such as liar, clown, c*nt, etc)

4. the overall interactions received by these comments were recorded.

Results from the above two separate processes were then cross analyzed to inquire whether these peaks that simultaneously occurred were triggered by the same event and the nature of harassments (see Results section, over-time graphs).

Deep dive into #defundthebbc

For the UK section of the project, we dived into the #defundthebbc hashtag which emerged from the BBC dataset in the first place. Through Crowdtangle we queried "#defundthebbc, #defundbbc, defundthebbc" collecting posts from the same period of the Twitter analysis (25 Nov-5Jan). From collecting Facebook posts, a public page related to the hashtag emerged, and it seemed to contribute and lead the #defundthebbc campaign online. From the 43 posts collected we skimmed the >50 comments posts and for the 20 posts that were left, we proceeded with comment analysis. The comments collection was done manually by detecting the first 10 hate comments (listing 'recent ones' from Facebook) with three directions: (1) hate towards a precise journalist, (2) towards a media outlet, (3) towards journalism in a large sense. For each post, we collected the first 10 hate comments following this coding protocol: Comment text | Likes | Replies | Keyword | Direction | From page results, the hate movement against the BBC already seemed to be organized. Each post, in fact, included a self-produced meme to summarise the news against the BBC which the post is promoting (see Findings). Moreover, trying to map to what extent there is, or there is not, a consistent infrastructure of hate that promotes the campaign to defund the BBC, we used the CrowdTangle extension for Chrome which permitted us to map where a precise article linked in one of the @DefundtheBBC posts taken into account, was reshared by other pages or verified profiles.

4. Findings

Hashtags that were used in relation to a specific journalist were plotted over time. This revealed differences between male and female journalists in which male journalists seem to be more attacked in the context of their profession (this was more clear in the words used, not so much in the tags as these pertained to the topics covered by the journalist), whereas female journalists seemed to be targeted through references that relate to their physical appearances, see figures 1-4.

Figure 1 Hashtags over-time for Dutch journalist Wierd Duk (De Telegraaf, male). Tags pertain mainly to the pandemic and topics covered by Duk. The orange tags are pertaining to him being a 'whorenalist' and the like...

Figure 2: hashtag-user network of the dataset pertaining to Asha ten Broeke (female Dutch journalist, de Volkskrant). Note how her physical appearance gets dragged in through tags such as fat shaming and BMI. This is also the case for the UK journalist Laura Kuenssberg, see figure 4, although this is almost not visible through the tags (see also the limitations section).

Figure 3: Tags and users surrounding male journalist Maarten Keulemans (de Volkskrant, note that this is the same paper as Asha ten Broeke in figure 2).

Figure 4: Hashtags over time in the dataset on female journalist Laura Kuenssberg (BBC). In the words in tweets and especially so in Facebook comments (see figure X) her facial features were frequently mentioned in the context of digital violence. In the tags, in figure 3 Laura is mentioned as Tory Laura pertaining to her perceived biased stances. Also interesting is that there are overall more tags relating to attacks on the press in general (the red tags) than in the visualization of hashtags over time for Wierd Duk.

Figure 5: hashtags over time for Daniel Matamal (male Chilean journalist). Here the attacks are also pertaining to critique on the press in general (#prensabasure means garbage press). Attacks on him pertain to more normative allegations on what a journalist should and should not be (Matamala lies).

As far as the influence of the perceived political leanings of media outlets, we could not derive conclusions through interpreting the user/tag networks. See figure 6 of NOS and Telegraaf, both Dutch outlets, perceived as pro-government (NOS) and rightwing/conservative (Telegraaf). Both hold tags such as msm (mainstream media, mostly used as attack) and fake news or even state propaganda, however, we would need more context of how these words are used and in what direction they are used. The hypothesis is that in the Telegraaf dataset, the uses of msm are mostly used to attack other media (other than Telegraag) whereas sich tags in the NOS dataset would be directed to NOS itself.

Figure 6 tags and users of the NOS dataset (left) and the Telegraaf dataset (right).

Figure 7: Interaction intensities over time with Laura Kuenssberg on Twitter and Facebook. Both platforms show the co-occurrence of Laura and defundthebbc. However, the qualitative dive showed that in Facebook, defund the BBC got its peak for different contents which are not the same as Twitter, which direct us towards new research paths of inquiring the ways of organizing and associating journalists when it comes to the platform. Furthermore, some peaks related to Laura did not occur due to the same source/event/news, though they happened during the same time period. Users have selected different sources/articles to show their anger toward the journalist.

The qualitative inquiry into the Facebook comments shows how most of the hate speech targeted at Laura was based on her personal traits (mouth) and then based on her political ideology. Furthermore, it is interesting that references to personal traits in hate speech occurred regardless of the nature of the event/news she reported. For instance articles about her as well as her own articles attracted a similar amount of hate speech targeted at personal traits.

Figure 8: Contextualized bubble diagram of most used words in Facebook comments connected to posts that pertained to Laura Kuenssberg. Note how her facial expressions are the main theme here.

Figure 9. Network of Facebook pages (yellow nodes) sharing similar news content (blue nodes). Made with the CrowdTangle plugin extension. Following the top ten most interacted news articles shared on the Defund the BBC Facebook page, which was also featured as a significant hashtag on Twitter. These kinds of analyses provide for a sense of the Facebook page ecology and how pages use (similar) news articles to further their causes.

5. Discussion and limitations

Journalists in the Netherlands were attacked in very individual ways, therefore it was impossible to distill a general Dutch hate lexicon as hate words were so specifically /'tailored' to the journalist at hand. Almost none of the hate words were used as hashtags, which made networks analyses using hashtag-user outputs less insightful. This was also an important limitation of the project. In the future, we would do good to network users and words in a sensible way. For the media outlet datasets, like with the journalists, the hashtags mainly focus on the pandemic, while hate words are used in ‘normal’ text and pertained to attacks on the functioning of the press in general.

The structure of the data and curation yielded some analytical difficulties for us. As violence seems to get hashtagged less and is more prominent in untagged words, we needed a different network output which is actually realized later in the week so this could be helpful for the future.

Scraping comments was not possible so we had to manually follow the posts that we gathered through CrowdTangle. This meant it was hard to scale up sufficiently so that we could assess the influence of the different platform affordances (Facebook's reactions buttons and its usage in relation to hateful comments) on the kinds of attacks and the extent of it on Facebook and Instagram.

Another limitation emerged from the structure of the data scraped through 4cat (Twitter handles and replies). Sometimes a user is replying to the content of an article while mentioning a journalist or news outlet. Sometimes a user is replying to another user, who was saying something about the article of a certain journalist or news outlet before. This leads to finding a lot of hits with certain hate speech that actually does not concern the journalist or news outlet. This should be kept in mind when interpreting the results of the data analysis. Example: replying to a user (from Dutch news outlet NOS): Translation: “@AliveCheis @NOS Look better, whiner. If you would check NOS headlines as much as you would call out that the NOS is not delivering any independent news, you would be much better up to date.”

Another limitation is sarcasm, as some positive words like ‘great’ in the query will result in finding tweets that are indeed positive or tweets that are actually meant in a bad way.
Example sarcasm (from Dutch news outlet NOS): Translation: “Nice that our great independent journalists from @RTLnieuws and @NOS pay so much attention to the leaked documents of @PvanHouwelingen and @GideonvMeijeren about the relationship between the WEF and members of parliament.”

User strategies in tagging journalists also implicated the interpretation of the data: users tag journalists for different reasons, ie. to draw them to their causes, stances, or to construct parasocial relationships. Alongside that the attacks take place which 'hides' those attacks in the reply threads. It makes our data messy but also tells us a lot about user strategies in tagging (the journalist is a victim in some way in both cases, either he/she is attacked directly or you get aligned with particular political-ideological leanings that you do not want to get aligned with as a journalist).

6. Conclusions

This project proffered preliminary findings that point us to various different future research directions. Interesting about Chile was that there seemed to be less direct attacks on journalists and if they were attacked they were more normative in nature (as a journalist you should be more like this and this) as compared to the UK and the Netherlands, where attacks seem to be more personal (based on personal character traits).

Another interesting point is the bidirectionality of hate: Attacks on journalists could be general but also refer to ties to the government. This is resulting in attacks from both sides. This is exemplified in the case of the journalist Amelia Gentleman who uncovered the Windrush immigration scandal. She got attacked by conservatives for uncovering the scandal (“race traitor) as well as by liberals for family ties to PM Boris Johnson (“hiding the story”).

Interesting future research directions to pursue seem to be hashtags and their different uses across platforms (for example tags such as defund the BBC and prensa basura (garbage press) seemed to used by different groups and in different political ways on Twitter and Facebook.

An interesting field to further explore is how legit news media get entangled with niche Facebook groups who share similar content in, perhaps, very different ways. See figure 9. Similar news articles will be (as hypothesized) contextualized in very different ways by different pages and/or Twitter accounts. If we scale up data and if we can automatically get to comments on Facebook and Instagram we can pursue more robust and generalizable findings on how hate against journalists gets 'allowed by' or afforded through various platforms. How do people use Reactions and replies to comments to align or diverge themselves from such attacks and what role do journalists play themselves in this context?

7. References

Berry, J. & Sobieraj, S. (2014). The outrage industry. Oxford University Press.

Bossetta, M. (2018). The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election. Journalism & Mass Communication Quarterly, 95(2), 471–496. https://doi.org/10.1177/1077699018763307

Halpern, D., Gibbs, J. (2013). Social media as a catalyst for online deliberation? Exploring the affordances of Facebook and YouTube for political expression. Computers in Human Behavior, 29, 1159-1168. doi:10.1016/j.chb.2012.10.008

Theocharis, Y., Cardenal, A., Jin, S., Aalberg, T., Hopmann, D. N., Strömbäck, J., Castro, L., Esser, F., Van Aelst, P., de Vreese, C., Corbu, N., Koc-Michalska, K., Matthes, J., Schemer, C., Sheafer, T., Splendore, S., Stanyer, J., Stępińska, A., & Štětka, V. (2021). Does the platform matter? Social media and COVID-19 conspiracy theory beliefs in 17 countries. New Media & Society. https://doi.org/10.1177/14614448211045666

Miller, K.C. (2021) Hostility Toward the Press: A Synthesis of Terms, Research, and Future Directions in Examining Harassment of Journalists, Digital Journalism, DOI: 10.1080/21670811.2021.1991824

Miller, K.C. (2021) Harassment’s Toll on Democracy: The Effects of Harassment Towards US Journalists, Journalism Practice, DOI: 10.1080/17512786.2021.2008809

Munger, K. (2017). Experimentally reducing partisan incivility on Twitter. Unpublished working paper. Available at: https://kmunger. github. io/pdfs/jmp. pdf.

Petre, C. (2015). The traffic factories: Metrics at chartbeat, gawker media, and the New York Times.

Christin, A., & Petre, C. (2020). Making peace with metrics: Relational work in online news production. Sociologica, 14(2), 133-156.

Silvio Waisbord (2020) Mob Censorship: Online Harassment of US Journalists in Times of Digital Hate and Populism, Digital Journalism, 8:8, 1030-1046, DOI: 10.1080/21670811.2020.1818111

Avery E. Holton, Valérie Bélair-Gagnon, Diana Bossio & Logan Molyneux (2021) “Not Their Fault, but Their Problem”: Organizational Responses to the Online Harassment of Journalists, Journalism Practice, DOI: 10.1080/17512786.2021.1946417

Seth C. Lewis, Rodrigo Zamith & Mark Coddington (2020) Online Harassment and Its Implications for the Journalist–Audience Relationship, Digital Journalism, 8:8, 1047-1067, DOI: 10.1080/21670811.2020.1811743

Rathje, S., Van Bavel, J. J., & Linden, S. (2021). Out-group animosity drives engagement on social media. In Proceedings of the National Academy of Sciences, 118(26). https://doi.org/10.1073/pnas.2024292118

Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353

https://www.niemanlab.org/2022/01/traffic-whoring-or-simply-optimizing-finding-the-boundaries-between-clean-and-dirty-metrics/ recent blog post by Caitlin Petre on the use of metrics in newsrooms

https://www.asc.upenn.edu/news-events/news/journalism-outdated-professor-barbie-zelizer-discusses-new-manifesto