Links table
Abstract and 1 introduction
2 background
3 systematic review
4 Determining meters
4.1 COUNTERSPECH classification
5 Impact of meters
6 accounting methods for transit seat data groups and 6.1
6.2 Curricula for meters detection and 6.3 approaches to generate meters
7 future views
8 Conclusion, declarations, and references
a summary
CounterSpeech provides direct refute to hateful speech by challenging the perpetrators of hate and showing support for abuse goals. It provides a promising alternative to more controversial measures, such as moderate content and formation, by contributing to a greater level of positive speech via the Internet instead of trying to reduce harmful content through removal. The progress in developing large language models means that the process of producing meters can be more efficient by automating its generation, which enables campaigns widely online. However, we are currently lacking a systematic understanding of many important factors related to the effective meters to reduce hatred, such as the most effective types of meters, what optimum conditions for implementation, and any specific hate effects can be better mitigated. This paper aims to fill this gap by reviewing Counspeesh research systematically in social sciences and comparing methodologies and results with computer science efforts to generate automatic meters. By taking this multidisciplinary view, we define promising future trends in both fields.
1 introduction
Exposure to users of social media on hate and abuse online is still a cause of public anxiety. Social media abuse is still important in terms of divorced (Vidgen et al The receiving of abuse can have negative effects on the mental health of the goals, as well as on others who witnessed it (Siegel, 2020; SAA ET Al., 2019). In the context of general numbers, it can be said that the influence on witnesses (passers -by) is more important, as it is likely to witness ill -treatment by a large number of people. In addition, politicians and other prominent actors from the public domain are expelled specifically due to the glass they receive on a daily basis (news, 2018), raising concerns about the health of public democracy.
Within this context, research on anti -abuse mechanisms has become more important than ever. One of this research angle is the field of “counterspeech” (or counter control): the content designed to resist or contradict abusive or hateful content (Benish, 2014 A; Soltman and Rossel, 2014; Bartlette and Cracodsk-Juns, 2015), we also see Figure 1. Interventions from the platform or from the application of the law, and may contribute to alleviating the effects of ill-treatment (Munger 2017, 2017; Buerger, 202B; Several Countspeech used to challenge hate directly, and Facebook has launched campaigns with local communities and policy makers to enhance access to Counsicalch tools.[2] Likewise, Moonshot and Jigsaw have carried out the method of re -guidance, as it presented alternative videos or counter videos when users search for queries that might suggest a tendency towards content or extremist groups.[3]
Discovering and generating meters is important because it supports the promise of auxiliary tools to relieve hatred. Determination of meters is also vital for analytical research in the region: for example, to separate the dynamics of perpetrators, victims and passers -by (Mathew et al
COUNTERSTECH production automatically is important and important for two reasons. First, Counsicalch is a time consumer and requires great experience to be effective (Chung et al., 2021C). Recently, large language models have been able to produce fluent arguments and character designed for user’s expectations that deal with various topics and tasks. Consequently, the development of Counsicalch tools is possible and can provide support for civil organizations, practitioners and stakeholders in widespread hatred. Second, by partially automating the meter writing, these auxiliary tools can reduce the psychological strain of practitioners resulting from the long exposure to the harmful content (Riedl Et al., 2020; Chung et al
However, despite the possibility of meters, and the increasing working group in this field, the search agenda remains relatively new, which also suffers from the fact that they are divided into a number of disciplinary silos. Systemically, at the same time, sociologists are studying the dynamics and effects of meters (such as munger, 2017; Buerger, 2021b; Hangartner et al., 2021; Bilewicz et al ;
The goal of this review article is to fill this gap, by providing a comprehensive multidisciplinary overview of the field of meters that cover computer science and social science over the past ten years. We offer a number of contributions in particular. First, we clarify the definition of the meters and a working frame to understand its use and effect, as well as a detailed classification. We review research on the effectiveness of the meters, with a combination of views on the effect it causes when it is experienced. We also analyze the artwork on Counsicalch, and specifically look at the task of generating meters, expansion, availability and methodology behind different data collections. More importantly, in all studies, we focus on common denominators and differences between computer science and social sciences, including how to assess the effect of meters and any specific effect of hate speech that relieves them better.
We benefit from the results we have reached to discuss the challenges and trends of open science (and AI safe) to reduce hate over the Internet. We make evidence of evidence for automatic curricula of meter tools using NLP processing (NLP). Likewise, for sociologists, we developed future views on multidisciplinary cooperation with artificial intelligence researchers on relieving online damage, including conducting widespread analyzes and assessing the effect of automatic interventions. Combating, our work provides researchers, policy makers and practitioners tools to increase the understanding of the mechanical meter capabilities to reduce hate over the Internet.
[2] https://counspeech.fb.com/en/ [3] https://moonshotteam.com/the-redirect-thod/
