Mapping online hate: A scientometric analysis on research trends and hotspots in research on online hate

Abstract

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society.

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

“…As outlined in recent Big Data Analytics studies (Huang et al, 2020;Odic and Wojcik, 2019;Chavatzia, 2017), differences in gender discrimination are strongly influenced by distorted mindsets, with deep, often hidden, repercussions over pay gaps (Courey and Heywood, 2018) and sexual harassment (Karami et al, 2020). Combining these findings with the ever-increasing influence of social media over real life (Jansen et al, 2009;Waqas et al, 2019;Stella et al, 2018b;Nasar et al, 2019) highlights an urgent necessity for using information processing in order to understand "if" and "how" specific massive online social platforms promote information on distorted mindsets. The tackling of such research question is essential for countering gender biases with data-informed approaches (Huang et al, 2020).…”

“…However, these approaches mainly focused on detecting differences in language use among different genders. Differently from other information processing investigations aiming at identifying emotions on social media in relation to phenomena like hateful speeches (Waqas et al, 2019) or disinformation spreading (Pierri et al, 2020), gender-focused investigations of social media did not explore large-scale mappings of the online perception of the gender gap in science as embedded in the messages exchanged between social users of any gender. Within an information management setting, the work closest to an investigation of the overall mindset about gender biases was the study by Karami and colleagues (Karami et al, 2020).…”

“…Perception is in the mind of the beholder. Every experience contributes to building a memory or mental 30 reconstruction of the outer world which, in turn, deeply impacts future behaviour (Malt et al, 2010; self-branding and journalistic content also through social bots (Varol and Uluturk, 2020), the spread of disinformation (Pierri et al, 2020) and the fostering of online hate dissemination (Waqas et al, 2019).…”

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

“…However, these approaches mainly focused on detecting differences in language use among different genders. Differently from other information processing investigations aiming at identifying emotions on social media in relation to phenomena like hateful speeches (Waqas et al, 2019) or disinformation spreading (Pierri, Artoni & Ceri, 2020), gender-focused investigations of social media did not explore large-scale mappings of the online perception of the gender gap in science as embedded in the messages exchanged between social users of any gender. Within an information management setting, the work closest to an investigation of the overall mindset about gender biases was the study by Karami and colleagues (Karami et al, 2020).…”

“…Data can be gathered by mining social media (Stella, Ferrara & De Domenico, 2018;Bovet, Morone & Makse, 2018), which represent an invaluable source of information about the users' experiences and perceptions of specific topics (Welles & González-Bailón, 2020). Recently, social media, Twitter in particular (Jansen et al, 2009), have been increasingly analysed by the scientific community in order to detect complex phenomena such as the emotional dynamics of voting events (Stella, Ferrara & De Domenico, 2018;Bovet, Morone & Makse, 2018), the promotion of self-branding and journalistic content also through social bots (Varol & Uluturk, 2020), the spread of disinformation (Pierri, Artoni & Ceri, 2020) and the fostering of online hate dissemination (Waqas et al, 2019).…”