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Exponential growth in social media has led to the increasing popularity of hate speech and hate-based propaganda. Hate speech or malicious expression refers to the use of offensive, violent, or offensive language and its religious conduct with a specific group of people who share a common property, such as gender, ethnicity, race, or beliefs. Online hate diffusion has now become a serious problem as it creates a series of international initiatives aimed at defining problems and developing effective countermeasures; this study delves into the exploration of the intention of hate speech posting on social media, especially on Twitter. Both dramaturgical models of social interaction and cultivation theory were used to explain the hate speech culture phenomenon. A qualitative method is proposed for this study as part of the exploration. Results revealed that most of the previous studies on hate speech focused on the field of computer science but rarely on the communication field. The paper presents the results of past studies and shows the new proposed framework. The investigation suggests future directions for the problem and possible solutions; it starts with the background of the research, the explanation of the problem, the meaning of the research, and pursuing the research questions and goals of the research before finally explaining the limits.

Exponential growth in social media has led to the increasing popularity of hate speech and hate-based propaganda. Hate speech or malicious expression refers to the use of offensive, violent, or offensive language and its religious conduct with a specific group of people who share a common property, such as gender, ethnicity, race, or beliefs. Online hate diffusion has now become a serious problem as it creates a series of international initiatives aimed at defining problems and developing effective countermeasures; this study delves into the exploration of the intention of hate speech posting on social media, especially on Twitter. Both dramaturgical models of social interaction and cultivation theory were used to explain the hate speech culture phenomenon. A qualitative method is proposed for this study as part of the exploration. Results revealed that most of the previous studies on hate speech focused on the field of computer science but rarely on the communication field. The paper presents the results of past studies and shows the new proposed framework. The investigation suggests future directions for the problem and possible solutions; it starts with the background of the research, the explanation of the problem, the meaning of the research, and pursuing the research questions and goals of the research before finally explaining the limits. [ABSTRACT FROM AUTHOR]

The internet has revolutionised our ability to communicate and connect across historic social, political and geographic divides. Where previously gatekeepers mitigated and negotiated access to mass media platforms, today potentially anyone – and any content – can reach millions of users in an instant. This development bears great opportunities for the democratisation of expression and the diversification of public discourse but has likewise broadened the impact of harm caused online. This raises the question how platforms and services can be regulated effectively to combat online harms without jeopardising free and open discourse. The paper explores the Online Harms White Paper published by the UK Government earlier this year and compares its regulatory approach with the infamous German Network Enforcement Law. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to provide an overview of South African perspectives on preventing, monitoring and combating hate victimisation, towards informing international understandings. Design/methodology/approach: Using a general review approach, this paper provides a historical examination of measures proposed by the South African Government and civil society since 1994, to prevent, monitor and combat hate crime, hate speech and intentional unfair discrimination. Findings: Regardless of a constitutional commitment to social inclusion, diversity and minority rights, significant progress remains lacking after almost three decades of related advocacy, lobbying and limited government intervention. Findings of the South African Hate Crimes Working Group (HCWG) longitudinal Monitoring Project emphasise the need for decisive legal responses to hate victimisation. Social implications: A Bill, recognising hate crime and hate speech as distinct criminal offences, has been in development for almost 15 years and will soon serve before Parliament. Enactment of this legislation will be ground-breaking in Africa. Originality/value: This paper contributes to the field of hate studies by providing an overview of the journey towards current conceptual understandings of hate in (South) Africa. It sets the stage for evaluating the potential of the redesigned HCWG monitoring tool, which holds promise for early identification and intervention in hate hotspots and targeted sectors. This instrument can establish trends not only in South Africa but also across the African continent. [ABSTRACT FROM AUTHOR]

Emotion identification from text can help boost the effectiveness of sentiment analysis models. Sarcasm is one of the more difficult emotions to detect, particularly in textual data. Even though several models for detecting sarcasm have been presented, their performance falls way short of that of other emotion detection models. As a result, few strategies have been introduced in the paper that helped to enhance the performance of sarcasm detection models. To compare performance, the model was tested using the TweetEval benchmark dataset. On the TweetEval benchmark, the technique proposed in this paper has established a new state-of-the-art. Besides the low performance, interpretability of existing sarcasm detection models are lacking compared to other emotion detection models like hate speech and anger. Therefore, an attention-based interpretability technique has been proposed in this paper that interprets the token importance for a certain decision of sarcasm detection model. The results of the interpretability technique aid in our comprehension of the contextual embeddings of the input tokens that the model has paid the greatest attention to while making a particular decision which outperforms existing transformer-based interpretability techniques, particularly in terms of visualisations. [ABSTRACT FROM AUTHOR]

Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely used machine-learning-based research differs in terms of the datasets, training methods, performance evaluation, and comparison methods used. In this paper, we surveyed 224 papers published between 2003 and 2022 that employed machine learning for text classification. The Preferred Reporting Items for Systematic Reviews (PRISMA) statement is used as the guidelines for the systematic review process. The comprehensive differences in the literature are analyzed in terms of six aspects: datasets, machine learning models, best accuracy, performance evaluation metrics, training and testing splitting methods, and comparisons among machine learning models. Furthermore, we highlight the limitations and research gaps in the literature. Although the research works included in the survey perform well in terms of text classification, improvement is required in many areas. We believe that this survey paper will be useful for researchers in the field of text classification. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to identify the anticipated political and social consequences for the security of Poland and its citizens due to the presence of hate speech in public discourse. A morphological method was used in the research. The paper consists of an introduction, three chapters and a conclusion. Introduction outlines the research problem and presents elements of research methodology. The second part provides examples of hate speech against refugees, migrants, national minorities, people with different culture, skin color, religion, while the third part characterizes attitudes and behaviors expressed in hate speech against LGBT people. The fourth part characterizes examples of hate language used in the political debate taking place in Poland. The conclusion summarizes the research results obtained. [ABSTRACT FROM AUTHOR]

As the debate on how harmful online content translates into violent offline actions continues in the Global North, this debate should be enriched by data from the Global South. In Libya, the kidnapping of a female politician (Seham Sergiwa) in 2019 was arguably foreshadowed on social media, with some analysts warning of an imminent danger to her life when tracking social media discourse in June/July 2019. Following her abduction, the outcry was loud and reverberated outside of Libya. However, this was short lived and similar occurrences happen in Libya still. This paper contributes a systematic analysis of Facebook content in the months leading up to and after Sergiwa's disappearance. Using thematic content analysis to explore 259 posts and 4,367 comments – between January and December 2019 – this paper outlines three dominant themes: Contempt, ridicule and indifference. This study presents a refocus on the dangers of online speech while acknowledging complexities around categorising the former. The study adds nuanced understandings on online discussions in Libya and gendered dynamics in a violent, post-revolutionary environment. [ABSTRACT FROM AUTHOR]

The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the "SentiSK" dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The "SentiSK" dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the "SentiSK" and "Sentigrade" datasets involved three classes (positive, neutral, and negative). In the second task, testing on the "SentiSK", "Sentigrade", and "Slovak dataset for SA" datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%. [ABSTRACT FROM AUTHOR]

Natural language processing's subfield of sentiment analysis involves locating and categorizing the feelings, viewpoints, and attitudes expressed in text. Because it enables us to understand public opinion on a variety of topics, sentiment analysis has grown in importance as social media platforms become more widely used. In this research paper, we used two deep learning models, BERT and RoBERTa, and their fusion of both architectures to perform sentiment analysis on a dataset of tweets related to the dataset of COVID-19 pandemic. To eliminate noise and unrelated data, the dataset underwent pre-processing and cleaning. Then, using the dataset, we trained the BERT and RoBERTa models and assessed their performance. Both models achieved high F1 scores, recall, and accuracy for all three sentiment classes (negative, neutral, and positive) for sentiment analysis. While there were some differences in how well these models performed across these metrics, both models did well and classified the sentiment of tweets in the dataset with high accuracy. Our study's findings show how well BERT and RoBERTa perform sentiment analysis on tweets about the COVID-19 pandemic. Our study also emphasizes how crucial it is to clean up and pre-process the dataset to get rid of extraneous data and noise that can harm the models' performance. The effectiveness of these models on datasets from other domains and topics will be examined. Future studies should also look into the models' interpretability and comprehend the features and patterns crucial to sentiment analysis. This paper emphasizes how we can avoid disaster tweets and be cautious to identify hate speech that disturbs the harmony in society. [ABSTRACT FROM AUTHOR]

Purpose: There is a scarcity of research studies on why people remain inactive when encountering and recognising misinformation online. The main aim of this paper is to provide a groundwork for future research into why users do not challenge misinformation on digital platforms by generating hypotheses through a synthesis of pertinent literature, including organisational behaviour, communication, human-computer interaction (HCI), psychology and education. Design/methodology/approach: Given the lack of directly related literature, this paper synthesised findings from relevant fields where the findings might be relevant, as the tendency to withhold opinions or feedback is a well-documented practice in offline interaction. Findings: Following the analysis of relevant literature, the potential reasons for online silence towards misinformation can be divided into six categories: self-oriented, relationship-oriented, others-oriented, content-oriented, individual characteristics and technical factors. Originality/value: Although corrections coming from peers can effectively combat misinformation, several studies showed that people in cyberspace do not take such action. To the best of the authors' knowledge, there has been scarce and virtually non-existent research investigating why people refrain from challenging others who post misinformation online. Thus, this paper attempts to address this gap and identify reasons in adjacent domains. The reasons provide a starting point for researching interventions to reduce reluctance and abstinence regarding the challenge of misinformation. The findings can be beneficial beyond the area of challenging misinformation and are extensible to other types of content and communication that people are hesitant to discuss and challenge, such as online injustice, prejudice and hate speech. [ABSTRACT FROM AUTHOR]

The machine learning (ML) research community has landed on automated hate speech detection as the vital tool in the mitigation of bad behavior online. However, it is not clear that this is a widely supported view outside of the ML world. Such a disconnect can have implications for whether automated detection tools are accepted or adopted. Here we lend insight into how other key stakeholders understand the challenge of addressing hate speech and the role automated detection plays in solving it. To do so, we develop and apply a structured approach to dissecting the discourses used by online platform companies, governments, and not-for-profit organizations when discussing hate speech. We find that, where hate speech mitigation is concerned, there is a profound disconnect between the computer science research community and other stakeholder groups—which puts progress on this important problem at serious risk. We identify urgent steps that need to be taken to incorporate computational researchers into a single, coherent, multistakeholder community that is working towards civil discourse online. [ABSTRACT FROM AUTHOR]

Background: The main topic of this scientific paper is based on what hate speech is, and how it affects young Albanians. The main problem raised in this paper is how hate speech is not much talked about in our everyday lives, and how young people do not have enough information about different examples of hate speech in their everyday lives and therefore not being aware how this can affect their mental health and emotional well - being. Purpose: The hypothesis that this scientific paper is based on is how today's society faces a current situation of co-existence, where there is a lack of information on hate speech, and how young people (students) do not know how to identify what can be considered hate speech or not. Method: The theoretical basis of this scientific paper focuses on relevant literatures, studies conducted on this topic from all over the world, as well as previous studies focused on Albania, with relevant references. This paper contains the main study conducted by the author, a survey named "Hate speech and its effect on emotional Wellbeing" with open and closed questions, the data of which have been analyzed and interpreted. Conclusion: The creation of new platforms where young people can be informed about hate speech, report experiences is a necessary need. [ABSTRACT FROM AUTHOR]

In Kosovo, ethnic identity played a central role in sustaining interethnic conflict until the current decade. Despite the efforts of the international community to reconcile the parties and mediate the political dialogue with Serbia, mistrust between ethnicities has been sustained by a history of tensed relations, identity politics, but also, to a significant extent, through the media and after the digitalization of communication - through social media. The paper problematizes on the role that mass media and social media have played in the ethnicization of conflicts and manifestation of ethnic hate speech, arguing that while the former has been subject to regulation and professionalization, the latter remain to a significant extent spaces that support the continuation of conflict and even the mobilization of groups. In describing the framework of interethnic relations in Kosovo, the history of interethnic relations and the dynamics of post-war events are briefly presented. Subsequently, through the case study and the method of documentary analysis, the paper addresses the role played by the media in amplifying conflicts in the case of the events of March 2004 and the more recent contributions of social networks in the unhindered promotion of ethnic hatred in the case of a political subject - the import of Serbian products into Kosovo. Finally, an argument is built in favour of the need for a more proactive approach of international organizations and local institutions in Kosovo in combating ethnic hate speech promoted via social networks, which can increase tensions in the fragile security environment of the Western Balkans. [ABSTRACT FROM AUTHOR]