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Polarization is implicated in the erosion of democracy and the progression to violence, which makes the polarization properties of large algorithmic content selection systems (recommender systems) a matter of concern for peace and security. While algorithm-driven social media do not seem to be a primary driver of polarization at the country level, they could be a useful intervention point in polarized societies. This paper examines algorithmic depolarization interventions aimed at transforming conflict: not suppressing or eliminating conflict, but making it more constructive. Algorithmic intervention is considered at three stages: what content is available (moderation), how content is selected and personalized (ranking), and content presentation and controls (user interface). Empirical studies of online conflict suggest that not only could the exposure-diversity intervention proposed as an antidote to ‘filter bubbles’ be improved: under some conditions, it can even worsen polarization. Using civility metrics in conjunction with diversity in content selection may be more effective. However, diversity-based interventions have not been tested at scale, and may not work in the diverse and dynamic contexts of real platforms. Instead, intervening in platform polarization dynamics will likely require continuous monitoring of polarization metrics, such as the widely used ‘feeling thermometer’. These metrics can be used to evaluate product features, and can potentially be engineered as algorithmic objectives. While using any metric as an optimization target may have harmful consequences, to prevent optimization processes from creating conflict as a side effect it may prove necessary to include polarization measures in the objective function of recommender algorithms.

Mass transit systems are relied on a daily basis to transport millions of passengers and bring billions of dollars’ worth of economic goods to market. While some forms of mass transit rely on a fuel, electrified railway systems are dependent on the electric grid. The electric grid is becoming more vulnerable to disruptions, due to extreme weather, changing supply and demand patterns, and cyber-terrorism. An interruption to the energy supply of a railway infrastructure can have cascading effects on the economy and social livelihood. Resilient interconnected microgrids are proposed to maintain reliable operation of electri_ed railway infrastructures. An engineering design framework, and supporting methods and techniques, is proposed for an electrified railway infrastructure to be upgraded from its existing form, to one with resilient interconnected microgrids. The sizing of the interconnected microgrids is performed using an iterative sizing analysis, considering multiple resiliency key performance indicators to inform the designer of the trade-o_s in sizing options. Hierarchical control is proposed to monitor and control the interconnected microgrids. A multi-objective problem cast in the tertiary level of control is proposed to be solved using game theory. The proposed designs are modelled and simulated in Simulink. Four case studies of railway infrastructures in Canada and the United Kingdom are used to demonstrate the effectiveness of the proposed designs. While results for each case study vary, resilient interconnected microgrids for railway infrastructures demonstrates a reduced dependence on the electric grid. The examples here are all scalable and can perform within the framework of any available energy system. The results are both extremely impressive and promising towards a more resilient and stable energy future for our railway and other critical infrastructures.

VOX-Pol Blog Post.

Extreme, anti-establishment actors are being characterized increasingly as ‘dangerous individuals’ by the social media platforms that once aided in making them into ‘Internet celebrities’. These individuals (and sometimes groups) are being ‘deplatformed’ by the leading social media companies such as Facebook, Instagram, Twitter and YouTube for such offences as ‘organised hate’. Deplatforming has prompted debate about ‘liberal big tech’ silencing free speech and taking on the role of editors, but also about the questions of whether it is effective and for whom. The research reported here follows certain of these Internet celebrities to Telegram as well as to a larger alternative social media ecology. It enquires empirically into some of the arguments made concerning whether deplatforming ‘works’ and how the deplatformed use Telegram. It discusses the effects of deplatforming for extreme Internet celebrities, alternative and mainstream social media platforms and the Internet at large. It also touches upon how social media companies’ deplatforming is affecting critical social media research, both into the substance of extreme speech as well as its audiences on mainstream as well as alternative platforms.

Extreme, anti-establishment actors are being characterized increasingly as ‘dangerous individuals’ by the social media platforms that once aided in making them into ‘Internet celebrities’. These individuals (and sometimes groups) are being ‘deplatformed’ by the leading social media companies such as Facebook, Instagram, Twitter and YouTube for such offences as ‘organised hate’. Deplatforming has prompted debate about ‘liberal big tech’ silencing free speech and taking on the role of editors, but also about the questions of whether it is effective and for whom. The research reported here follows certain of these Internet celebrities to Telegram as well as to a larger alternative social media ecology. It enquires empirically into some of the arguments made concerning whether deplatforming ‘works’ and how the deplatformed use Telegram. It discusses the effects of deplatforming for extreme Internet celebrities, alternative and mainstream social media platforms and the Internet at large. It also touches upon how social media companies’ deplatforming is affecting critical social media research, both into the substance of extreme speech as well as its audiences on mainstream as well as alternative platforms.

From politicians to podcast hosts, online platforms have systematically banned (“deplatformed”) influential users for breaking platform guidelines. Previous inquiries on the effectiveness of this intervention are inconclusive because 1) they consider only few deplatforming events; 2) they consider only overt engagement traces (e.g., likes and posts) but not passive engagement (e.g., views); 3) they do not consider all the potential places users impacted by the deplatforming event might migrate to. We address these limitations in a longitudinal, quasi-experimental study of 165 deplatforming events targeted at 101 influencers. We collect deplatforming events from Reddit posts and then manually curate the data, ensuring the correctness of a large dataset of deplatforming events. Then, we link these events to Google Trends and Wikipedia page views, platform-agnostic measures of online attention that capture the general public’s interest in specific influencers. Through a difference-in-differences approach, we find that deplatforming reduces online attention toward influencers. After 12 months, we estimate that online attention toward deplatformed influencers is reduced by -63% (95% CI [-75%,-46%]) on Google and by -43% (95% CI [-57%,-24%]) on Wikipedia. Further, as we study over a hundred deplatforming events, we can analyze in which cases deplatforming is more or less impactful, revealing nuances about the intervention. Notably, we find that both permanent and temporary deplatforming reduce online attention toward influencers; Overall, this work contributes to the ongoing effort to map the effectiveness of content moderation interventions, driving platform governance away from speculation.