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In this article, we conduct a comprehensive study of online antagonistic content related to Jewish identity posted on Twitter between October 2015 and October 2016 by UK-based users. We trained a scalable supervised machine learning classifier to identify antisemitic content to reveal patterns of online antisemitism perpetration at the source. We built statistical models to analyze the inhibiting and enabling factors of the size (number of retweets) and survival (duration of retweets) of information flows in addition to the production of online antagonistic content. Despite observing high temporal variability, we found that only a small proportion (0.7%) of the content was antagonistic. We also found that antagonistic content was less likely to disseminate in size or survive for a longer period. Information flows from antisemitic agents on Twitter gained less traction, while information flows emanating from capable and willing counter-speech actors—that is, Jewish organizations—had a significantly higher size and survival rates. This study is the first to demonstrate that Sampson’s classic sociological concept of collective efficacy can be observed on social media (SM). Our findings suggest that when organizations aiming to counter harmful narratives become active on SM platforms, their messages propagate further and achieve greater longevity than antagonistic messages. On SM, counter-speech posted by credible, capable and willing actors can be an effective measure to prevent harmful narratives. Based on our findings, we underline the value of the work by community organizations in reducing the propagation of cyberhate and increasing trust in SM platforms.

ObjectivesTo investigate concussion injury rates, the likelihood of sustaining concussion relative to the number of rugby union matches and the risk of subsequent injury following concussion.MethodsA four-season (2012/2013–2015/2016) prospective cohort study of injuries in professional level (club and international) rugby union. Incidence (injuries/1000 player-match-hours), severity (days lost per injury) and number of professional matches conferring a large risk of concussion were determined. The risk of injury following concussion was assessed using a survival model.ResultsConcussion incidence increased from 7.9 (95% CI 5.1 to 11.7) to 21.5 injuries/1000 player-match-hours (95% CI 16.4 to 27.6) over the four seasons for combined club and international rugby union. Concussion severity was unchanged over time (median: 9 days). Players were at a greater risk of sustaining a concussion than not after an exposure of 25 matches (95% CI 19 to 32). Injury risk (any injury) was 38% greater (HR 1.38; 95% CI 1.21 to 1.56) following concussion than after a non-concussive injury. Injuries to the head and neck (HR 1.34; 95% CI 1.06 to 1.70), upper limb (HR 1.59; 95% CI 1.19 to 2.12), pelvic region (HR 2.07; 95% CI 1.18 to 3.65) and the lower limb (HR 1.60; 95% CI 1.21 to 2.10) were more likely following concussion than after a non-concussive injury.ConclusionConcussion incidence increased, while severity remained unchanged, during the 4 years of this study. Playing more than 25 matches in the 2015/2016 season meant that sustaining concussion was more likely than not sustaining concussion. The 38% greater injury risk after concussive injury (compared with non-concussive injury) suggests return to play protocols warrant investigation.