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J. M Berger & Bill Strathearn, “Who matters online: Measuring influence, evaluating content, and countering violent extremism in online social networks”, ICSR, March 2013.
Introduction & Key Findings
It is relatively easy to identify tens of thousands of social media users who have an interest in violent ideologies, but very difficult to figure out which users are worth watching. For students of extremist movements and those working to counter violent extremism online, deciphering the signal amid the noise can prove incredibly daunting.
This paper sets out a first step in solving that problem. We have devised a scoring system to find out which social media accounts within a specific extremist circle were most influential and most prone to be influenced (a tendency we called exposure).
Our starting data centered on followers of 12 American white nationalist/white supremacist “seed” accounts on Twitter. We discovered that by limiting our analysis to interactions with this set, the metrics also identified the users who were highly engaged with extremist ideology.
Within our total dataset of 3,542 users, only 44 percent overtly identified themselves as white nationalists online. By measuring interactions alone—without analyzing user content related to the ideology—we narrowed the starting set down to 100 top-scoring accounts, of which 95 percent overtly self-identified as white nationalist. Among the top 200, 83 percent self-identified, and for the top 400, the self-identification rate was 74 percent. A comparison analysis run on followers of anarchist Twitter accounts suggests the methodology can be used without modification on any number of ideologies.
Because this approach is entirely new (at least in the public sphere), the paper spends some time discussing the methodology and findings in some detail, before concluding with a series of recommendations for countering violent extremism (CVE) based on the findings. The key terms for understanding the recommendations are:
- Influence: The tendency of a user to inspire a measurable reaction from other users (such as a replies or retweets).
- Exposure: The flip side of influence, this is the tendency of a user to respond to another user in a measurable way.
- Interactivity: The sum of influence and exposure scores, roughly representing how often a user interacts with the content of other users.
Our key findings include:
- Influence is highly concentrated among the top 1 percent of users in the set.
- High scores in both influence and exposure showed a strong correlation to engagement with the seed ideology (white nationalism in our primary analysis, and anarchism in a secondary analysis).
- Interactivity, the sum of influence and exposure scores, was even more accurate at identifying users highly engaged with the seed ideology.
In the course of collecting the data needed to measure influence and exposure, we incidentally collected a large amount of data on hashtags and links used by people who follow known white nationalists on Twitter. When we examined this data, we discovered that members of the dataset were highly engaged with partisan Republican and mainstream conservative politics. The paper presents a significant amount of context needed to properly evaluate this finding.
Working from these findings, the paper makes several recommendations for new CVE initiatives with a focus on NGO efforts, which was the purpose of this research, although we recognize our findings will likely have utilityfor government efforts in this sphere as well.
Our recommendations include:
- We believe these metrics offer ways to concretely measure which types of CVE approaches are effective and which are not, bringing some clarity to a realm where strategies are often wishful and based on assumptions, while conclusions are often anecdotal and inconclusive.
- The concentration of influence among a very few users suggests that disruptive approaches and counter-messaging should be targeted to the top of the food chain, rather than working with the larger base of users.
- Our analysis found that the seed accounts—all well-known white nationalist ideologues and activists—were not necessarily producing the most popular content and links to external Web sites. The collected data can be used to find the most important external content sources, and target them for disruption through terms-of-service violation reporting, or through counter-messaging.
- By tracking these metrics on an ongoing basis, NGO efforts to counterprogram against extremist narratives can be evaluated to measure how many users adopt or respond to counter-messaging content, and how much influence accrues to different kinds of positive messaging.
- Since the data suggests white nationalists are actively seeking dialogue with conservatives, CVE activists should enlist the help of mainstream conservatives, who may be considerably more successful than NGOs at engaging extremists with positive messaging. Further research may also suggest avenues for engagement between other kinds of extremists and other mainstream political and religious movements.
Finally, we believe that these metrics are only a starting point for the study of extremist use of social media. We believe the metrics and approaches here can be further refined, and we believe that additional research may yield substantial new techniques for monitoring and countering the promotion of violent ideologies online.