Online Library

The COVID-19 pandemic has brought about trauma and uncertainty for vast swathes of the world population, including in Australia. One effect of this has been the growth of COVID-19 conspiracy theories, and general conspiracism. This article explores efforts by fascists and neo-Nazis to exploit the rise in conspiratorial thinking for recruitment and dissemination of their ideas. Five Australian conspiracist Telegram channels are studied for signs of fascist cross-pollination, and it is found that users with fascist sympathies attempt to influence the channels’ discourse through appeals to purported ideological and situational commonalities.

The massacre of more than fifty worshippers at mosques in Christchurch, New Zealand, shocked the world. The murders were not random. They expressed a particular ideology, one that the alleged perpetrator described as ‘fascism’. But what does fascism mean today — and what kind of threat does it pose? Jeff Sparrow traces the history of the far right, showing how fascists have adapted to the new politics of the twenty-first century. Burgeoning in dark places online, contemporary fascism exults in violence and picks its targets strategically. Today, it is Muslims; tomorrow, it will be Jews or gays or Asians. With imitative massacres already occurring around the world, Christchurch must be a wake-up call. This book makes a compelling, urgent case for a new response to an old menace.

More than 550 Muslim women from Western countries have joined ISIS and moved to its proclaimed ‘Caliphate’ in Syria and Iraq. No extremist group has been able to attract so many female Western recruits so far, and their number continues to grow. This article is intended to explain the reasons behind such unprecedented success, the motivation of Western Muslims to join ISIS and their roles in the ‘Islamic State’. It also compares living conditions under ISIS’ rule with the expectation induced by ISIS’ recruiters in women from the West who had shown an interest to make hijra and join ISIS. Understanding these factors is vital to figure out how to stop this trend and to assess the security threat posed to the West by possible female returnees, or radicalised sympathizers who are unable to leave their countries of residence.

In recent decades, information technology went through an explosive evolution, revolutionizing the way communication takes place, on the one hand enabling the rapid, easy and almost costless digital interaction, but, on the other, easing the adoption of more aggressive communication styles. It is crucial to regulate and attenuate these behaviors, especially in the digital context, where these emerge at a fast and uncontrollable pace and often cause severe damage to the targets. Social networks and other entities tend to channel their efforts into minimizing hate speech, but the way each one handles the issue varies. Thus, in this thesis, we investigate the problem of hate speech detection in social networks, focusing directly on Twitter. Our first goal was to conduct a systematic literature review of the topic, targeting mostly theoretical and practical approaches. We exhaustively collected and critically summarized mostly recent literature addressing the topic, highlighting popular definitions of hate, common targets and different manifestations of such behaviors. Most perspectives tackle the problem by adopting machine learning approaches, focusing mostly on text mining and natural language processing techniques, on Twitter. Other authors present novel features addressing the users themselves. Although most recent approaches target Twitter, we noticed there were few tools available that would address this social network platform or tweets in particular, considering their informal and specific syntax. Thus, our second goal was to develop a tokenizer able to split tweets into their corresponding tokens, taking into account all their particularities. We performed two binary hate identification experiments, having achieved the best f-score in one of them using our tokenizer. We used our tool in the experiments conducted in the following chapters. As our third goal, we proposed to assess which text-based features and preprocessing techniques would produce the best results in hate speech detection. During our literature review, we collected the most common preprocessing, sentiment and vectorization features and extracted the ones we found suitable for Twitter in particular. We concluded that preprocessing the data is crucial to reduce its dimensionality, which is often a problem in small datasets. Additionally, the f-score also improved. Furthermore, analyzing the tweets’ semantics and extracting their character n-grams were the tested features that better improved the detection of hate, enhancing the f-score by 1.5% and the hate recall by almost 5% on unseen testing data. On the other hand, analyzing the tweets’ sentiment didn’t prove to be helpful. Our final goal derived from a lack of user-based features in the literature. Thus, we investigated a set of features based on profiling Twitter users, focusing on several aspects, such as the gender of authors and mentioned users, their tendency towards hateful behaviors and other characteristics related to their accounts (e.g. number of friends and followers). For each user, we also generated an ego network, and computed graph-related statistics (e.g. centrality, homophily), achieving significant improvements – f-score and hate recall increased by 5.7% and 7%, respectively.

This Element presents original research into how young people interact with violent extremist material, including terrorist propaganda, when online. It explores a series of emotional and behavioural responses that challenge assumptions that terror or trauma are the primary emotional responses to these online environments. It situates young people’s emotional responses within a social framework, revealing them to have a relatively sophisticated relationship with violent extremism on social media that challenges simplistic concerns about processes of radicalisation. The Element draws on four years of research, including quantitative surveys and qualitative focus groups with young people, and presents a unique perspective drawn from young people’s experiences.

Lessons learned after one year of the NetzDG law.