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Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.

This paper reports on a study that is part of a project which aims to develop a multimodal analytical approach for big data analytics, initially in the context of violent extremism. The findings reported here tested the application of natural language processing models to the text of a sample of articles from the online magazines Dabiq and Rumiyah, produced by the Islamic extremist organisation ISIS. For comparison, text of articles found by reverse image search software which re-used the lead images from the original articles in text which either reported on or opposed extremist activities was also analysed. The aim was to explore what insights the natural language processing models could provide to distinguish between texts produced as propaganda to incite violent extremism and texts which either reported on or opposed violent extremism. The results showed that some valuable insights can be gained from such an approach and that these results could be improved through integrating automated analyses with a theoretical approach with analysed language and images in their immediate and social contexts. Such an approach will inform the interpretation of results and will be used in training software so that stronger results can be achieved in the future.

This paper investigates the link between social media and hate crime. We show that antirefugee sentiment on Facebook predicts crimes against refugees in otherwise similar municipalities with higher social media usage. To establish causality, we exploit exogenous variation in the timing of major Facebook and internet outages. Consistent with a role for “echo chambers,” we find that right-wing social media posts contain narrower and more loaded content than news reports. Our results suggest that social media can act as a propagation mechanism for violent crimes by enabling the spread of extreme viewpoints.

This research paper examines and assesses how members of the Somali jihadi insurgent group al-Shabaab have attempted to influence the mass media for strategic communications purposes. Using the group’s activities between the years 2005 and 2017 as a case study, this paper asserts that al-Shabaab’s attempts to influence the mass media for news coverage purposes, despite its own operational security concerns since it withdrew from Mogadishu in 2011, is forward-looking, fast-paced, aggressive, and by and large successful. But the conclusions also assert that despite the group’s focused strategic communications and its opportunistic use of Propaganda of the Deed, its successes correlate directly to the failure of the poor and generally uncoordinated communications efforts of the international coalition working to counter it. The generally poor handling of strategic communications by an expensive combination of the Somali government, the African Union Mission to Somalia (AMISOM), the United Nations (UN), and some members of the international community has allowed the strategic communications of al-Shabaab a relatively free, unchecked, and unchallenged passage. In some cases related to major attacks, al-Shabaab’s strategic communications are even viewed by members of the mass media as more authentic, realistic, timely, and truthful, than those of those of the coalition working to counter it.

VOX-Pol Blog post.

VOX-Pol Blog post.