Internet, social media and online hate speech. Systematic review

Castaño-Pulgarín, Sergio Andrés; Suárez-Betancur, Natalia; Vega, Luz Magnolia Tilano; López, Harvey Mauricio Herrera

doi:10.1016/j.avb.2021.101608

Cited by 138 publications

(84 citation statements)

References 48 publications

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“…Rather than online behavior that is labeled as antisocial being limited to a small, vocal group of provocateurs, recent research has reported that standard OSN users can be provoked into becoming trolls or engaging in other antisocial behavior, influenced by the user's mood and the context of the discussion [315]. Similarly, communications labeled as hate speech can be triggered in users by their OSN community members' behaviors [316,317] as well as being more likely when specific topics are discussed [318,319]. Such research suggests that at least a subset of OSN users perceived as malevolent might become more positive contributors to OSNs by the thoughtful shaping of online environments.…”

Section: Discussionmentioning

confidence: 99%

Role-Aware Information Spread in Online Social Networks

Bartal

Jagodnik

2021

Entropy

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Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

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Section: Discussionmentioning

confidence: 99%

Role-Aware Information Spread in Online Social Networks

Bartal

Jagodnik

2021

Entropy

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“…The task-specific part acts as a single-sentence classifier on each dataset to classify their samples. The classifier is a fully connected layer followed by a softmax to estimate the probability of a sentence's contextual representation vector, which is labeled as class c as shown in Equation (1). The cross-entropy loss over the softmax output is used in our experiment to train the MTL model on both binary and multi-class classifications.…”

Section: Proposed Modelmentioning

confidence: 99%

“…The Cambridge Dictionary (https://dictionary.cambridge.org/fr/ (accessed on 10 May 2021)) defines hate speech as "public speech that expresses hate or encourages violence towards a person or group based on something such as race, religion, sex or sexual orientation". Online hate speech is characterized as the use of an offensive language, aimed at a specific group of people who share some common trait [1], while social networks have been recognized as a very favorable medium often used for planning and executing hate attack related activities [2]. Beyond the psychological harm, such toxic online content may be influencing and radicalizing individuals and could lead to actual hate crimes [3].…”

Section: Introductionmentioning

confidence: 99%

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

et al. 2021

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As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

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“…The online hate against women tends to use shaming. [...] flaming, trolling, hostility, obscenity, high incidence of insults, aggressive lexis, suspicion, demasculinization, and dehumanization can inflict harm" [51]. This information could be exploited to verify the diversity and representativeness of the samples collected in a dataset.…”

Section: Introduction Of Biasesmentioning

confidence: 99%

Automatic Identification of Harmful, Aggressive, Abusive, and Offensive Language on the Web: A Survey of Technical Biases Informed by Psychology Literature

Balayn

Yang

Szlávik

et al. 2021

Trans. Soc. Comput.

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The automatic detection of conflictual languages (harmful, aggressive, abusive, and offensive languages) is essential to provide a healthy conversation environment on the Web. To design and develop detection systems that are capable of achieving satisfactory performance, a thorough understanding of the nature and properties of the targeted type of conflictual language is of great importance. The scientific communities investigating human psychology and social behavior have studied these languages in details, but their insights have only partially reached the computer science community. In this survey, we aim both at systematically characterizing the conceptual properties of online conflictual languages, and at investigating the extent to which they are reflected in state-of-the-art automatic detection systems. Through an analysis of psychology literature, we provide a reconciled taxonomy that denotes the ensemble of conflictual languages typically studied in computer science. We then characterize the conceptual mismatches that can be observed in the main semantic and contextual properties of these languages and their treatment in computer science works; and systematically uncover resulting technical biases in the design of machine learning classification models and the dataset created for their training. Finally, we discuss diverse research opportunities for the computer science community and reflect on broader technical and structural issues.

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Internet, social media and online hate speech. Systematic review

Cited by 138 publications

References 48 publications

Role-Aware Information Spread in Online Social Networks

Role-Aware Information Spread in Online Social Networks

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Automatic Identification of Harmful, Aggressive, Abusive, and Offensive Language on the Web: A Survey of Technical Biases Informed by Psychology Literature

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