Analysis Of Hate Speech Research Articles

Sentiment Analysis of Hate Speech on Women in Social Media Platform Using Multi Label Classification

Sentiment Analysis of Hate Speech on Women in Social Media Platform Using Multi Label Classification

Sentiment Analysis of Hate Speech against DPR-RI on Twitter Using Naive Bayes and KNN Algorithms

According to surveys, the rise in social media users has resulted in an increase of hate speech, and Twitter is one of the most popular platforms for this type of speech. The tweet feature on Twitter enables users to make repeated instances of hate speech, making Twitter data very intriguing to analyze. This study aims to investigate whether a tweet contains hate speech towards the Indonesian House of Representatives (DPR-RI). The research employed crawling techniques to gather data from Twitter using the Twitter API feature. The Naïve Bayes algorithm was applied, and the results were compared with the accuracy of the K-Nearest Neighbor. After preprocessing, the total data obtained was 1,494, with 956 test data and 538 training data. The study revealed that Twitter users' sentiment towards DPR-RI was 49.2% positive and 50.8% negative sentiment when tested using Naïve Bayes. Meanwhile, KNN showed 23.4% positive and 76.6% negative sentiment. The high negative sentiment in both classifiers suggests that Twitter users frequently express hate speech towards DPR-RI. Naïve Bayes algorithm showed the highest prediction accuracy at 98.32%, while the K-Nearest Neighbor algorithm had an accuracy of only 62.84%.

A Sentiment Analysis of Hate Speech in Philippine Election-Related Posts Using BERT Combined with Convolutional Neural Networks and Model Variations Incorporating Hashtags and ALL-CAPS

As the number of people who use X continually increases, the same thing is true for hate speech. A pressing need exists for automatic detection of posts that promote hate speech. The datasets gathered and validated from the base study were used to categorize posts as either hate or non-hate and classify them as positive, negative, or neutral using Conventional Neural Networks. The partitioning of the labeled data into training and testing sets adhered to a ratio scheme: 70%-30%, 80%-20%, and 90%-10%. The model of this study, BERT-CNN, had an overall better performance than the base study, fastText CNN. Notably, among the three splits, the BERT-CNN model for binary classification without the features of Hashtags and ALL-CAPS with the 90:10 split achieved the best performance with an accuracy of 93.55%, precision of 93.59%, and F1-score of 93.55%. For multi-label classification, the BERT-CNN model demonstrated its optimal performance when incorporating hashtags, specifically with the 90:10 split, achieving an accuracy of 69.14%, precision of 68.44%, recall of 68.40%, and an F1-score of 67.41%. The innovative use of BERT word embeddings paired with CNN proved to excel in classifying Philippine election-related posts as hate or non-hate.

Echoes of Violence: Analyzing Hate Speech in the Ethiopian Broadcast Media

Echoes of Violence: Analyzing Hate Speech in the Ethiopian Broadcast Media

Enhancement of Sentiment Analysis of Hate Speech through Ensemble Classifier Model

Enhancement of Sentiment Analysis of Hate Speech through Ensemble Classifier Model

Analyzing hate speech dynamics on Twitter/X: Insights from conversational data and the impact of user interaction patterns

This paper investigates the pervasive issue of hate speech within Twitter/X Portuguese network conversations, offering a multifaceted analysis of its characteristics. This study utilizes a mixed-method approach, combining several methodologies of network analysis (triad census and participation shifts) over the network of interaction between users. Qualitative manual content annotation was applied to the dataset to dissect different patterns of hate speech on the platform. Key findings reveal that the number of users followed by an individual and potentially reads is a relevant predictor for a user's propensity to post aggressive content. We concluded also that during a conversation thread, hate speech happens significantly more within the first 2 h of interaction. Transitivity of interactions and individual expression are considerably lower as more hate speech is prevalent in conversations. Our research confirms that hate speech is usually expressed by external individuals who intrude into conversations. Conversely, the expression of hate speech of indirect type by third parties interfering in conversations is uncommon. We also found that counter-speech discourse is strongly correlated with a type of discourse that typically avoids conflict and is not privately held.

Analysis of Hate Speech in the Perspective of Changes to the Electronic Information and Transaction Law

This study aimed to assess the effectiveness of hate speech rules concerning changes in electronic transaction information policies. It further used normative legal study which included the process of identifying legal rules, principles, and doctrines to address the constitutional issues. The results showed that the amendments from the 2008 ITE Law to the 2016 ITE Law did not adequately the basic problems. Furthermore, certain forms of hate speech rules were observed in the ITE Law, particularly in Article 27 paragraph (3) and Article 28 paragraph (2). These rules were unclear benchmarks used in assessing social media posts as hate speech, defamation, or criticism and opinion from an individual about a problem. Therefore, making improvements through legal reform was essential to ITE Law, specifically Article 27 paragraph (3) and Article 28 paragraph (2). The amendments were necessary to develop good legal certainty for the community. The government should further make legal comparisons with other countries to identify the best regulatory framework for the community.

An Analysis of Hate Speech in Denise Chariesta's Instagram Comment Section

An Analysis of Hate Speech in Denise Chariesta's Instagram Comment Section

Contra-hate spin di media sosial twitter jelang pilpres 2019 dengan pendekatan strategi sun-tzu

This study aims to identify hate spins through hate speech content on Twitter social media during the 2019 Presidential Election campaign, and analyze patterns of hate spin movements, strategies and obstacles to overcome hate spins using the Sun Tzu approach. Research methods with content analysis to analyze hate speech and hate spins that occur on Twitter ahead of the 2019 Presidential Election, starting 15 February-15 April 2019. There are 36 (thirty-six) hate spins that attack candidate pairs 01 and 12 (twelve) hate spin attacking candidate 02. There are four cyber-attack patterns in the form of hate spins, namely: a wave and gradual hate spin attacks on the same candidate pair, single hate spin attacks, and hate spin attacks attacking each other between the two teams of candidates. Polri uses several counter-strategies: (1) using a plan to win; (2) a strategy of dealing directly with the enemy; (3) a strategy of chaos; and (4) a strategy of losing. The obstacles faced are the competent police personnel were limited and the discretionary conditions of security and order. Policy recommendations require additional personnel both in quantity and quality for information and communication technology (ICT).

An Analysis of Hate Speech in Lucas's Instagram Post: Pragmatics Study

Hate speech is a behavior, word, writing, or act that is prohibited because it can trigger acts of prejudice and violence on a victim, perpetrator of violence, or the statement. This research aims to investigate the types of hate speech and find out the meaning of these types of hate speech. This research takes data from Instagram and comments on @lucas_xx444. As a former member of NCT and WayV, the data was analyzed using qualitative methods. A multifaceted approach to comprehensively understanding and addressing hate speech, recognizing its social deviance, and advocating for collective efforts to create environments that prioritize respect, equality, and unity. The results of the research found six types of hate speech in the comments column: provoking, inciting, insulting, blasphemy, defamation, and spreading hoaxes.

Detection of Hate-Speech Tweets Based on Deep Learning: A Review

Cybercrime, cyberbullying, and hate speech have all increased in conjunction with the use of the internet and social media. The scope of hate speech knows no bounds or organizational or individual boundaries. This disorder affects many people in diverse ways. It can be harsh, offensive, or discriminating depending on the target's gender, race, political opinions, religious intolerance, nationality, human color, disability, ethnicity, sexual orientation, or status as an immigrant. Authorities and academics are investigating new methods for identifying hate speech on social media platforms like Facebook and Twitter. This study adds to the ongoing discussion about creating safer digital spaces while balancing limiting hate speech and protecting freedom of speech. Partnerships between researchers, platform developers, and communities are crucial in creating efficient and ethical content moderation systems on Twitter and other social media sites. For this reason, multiple methodologies, models, and algorithms are employed. This study presents a thorough analysis of hate speech in numerous research publications. Each article has been thoroughly examined, including evaluating the algorithms or methodologies used, databases, classification techniques, and the findings achieved. In addition, comprehensive discussions were held on all the examined papers, explicitly focusing on consuming deep learning techniques to detect hate speech.

Linguistic Analysis of Hate Speech on Social Media

This article aims at defining the essence of “Hate Speech” and studying it as a source of neologisms and metaphors depicted on social media. Concerns about the relationship between inflammatory hate speech and violent acts have grown as a result of the rising number of attacks against people of different national, ethnic and religious affiliations, cultures, orientations and beliefs. The fact that the language of social media is characterized by tendencies to disguise the truth, reveal half-truths and ambiguous expressions and influence public opinion makes us realize that people especially public figures spare no effort to find effective terminology or phraseology in order to cast a shadow, ridicule or humiliate the opposing side through linguistic means. Therefore, the analysis of such linguistic peculiarities of social media gain a great importance in order to avoid the use of irrelevant terminology aiming at insulting and humiliation people.

A Corpus Analysis of Hate Speech in Pakistani Political Discourse on Twitter

This paper is a corpus-based quantitative study on language change that has arisen during national political events, such as the no-confidence movement against Imran Khan, the Prime Minister of Pakistan. This event, including many others, has given rise to the hate speech in Pakistani community online. In this paper, language change is discussed in terms of hate speech, collocation patterns, frequency of words (Nouns, Adjectives), N-Grams and Urdu hate speech words. A certain political time period is selected to make a comparison of the use of language and explore whether any variations have transpired during the political upheavals. These dynamics are explored because the frequency of hate speech words may suggest that how much aggressive the online political discourse has become. Moreover, it may also suggest that what kind of image is presented to the rest of the world through such usage of words. To find out the case, a data of 50,000 tweets was gathered using Tweetarchivist.com. This data was analyzed through sketch engine. The major findings from the research, reported in this paper, show that many of the tweets, through their collocations and frequency, contain hate speech elements which convey a negative image of the country across the globe.

UJARAN KEBENCIAN WARGANET PADA AKUN INSTAGRAM @UK (UKI KAUTSAR): KAJIAN LINGUISTIK FORENSIK

Netizens Hate Speech on Instagram Account @UK (Uki Kautsar): Forensic Linguistics Study. This study aims to analyze hate speech contained in netizen comments on posts on the @UK Instagram account. This study uses a descriptive qualitative approach. The research data is hate speech in the form of comments by netizens on posts on the @UK Instagram account. The data source for this research is comments from netizens on posts on the @UK Instagram account. The data obtained and analyzed descriptively. Based on the results of data analysis, researchers found hate speech in the form of igniting and appeasing. Hate speech in the form of provocative actions is included in Article 160 of the Criminal Code. Hate speech in the form of hurtful acts can be subject to Article 315 of the Criminal Code. Hate speech is classified as hate speech in the form of abnormalities or disorders.

Mowa nienawiści a dobra osobiste – wybrane aspekty ochrony cywilnoprawnej

The subject of this article is the analysis of hate speech in civil law terms through the prism of the institution of personal rights. The author points out that the issue of hate speech is an interdisciplinary phenomenon, but one that is strongly related to legal sciences, in particular criminal law. It would be of great value to science to consider the topic more broadly from the perspective of administrative and civil law. The author links the issue of hate speech with personal rights and points out that, on civil law grounds, hate speech is an attack on specific personal rights. Then he discusses the essence of the regulation of personal rights. Next, he goes on to analyse the position of the entitled and responsible parties and extensively analyses claims arising from the violation of personal rights in the context of hate speech. The author also proposes solutions that could increase the level of civil law protection (i.e., the institution of a blind civil lawsuit) and points to the important role of legal education. According to the author, the Polish legal system provides civil law institutions that are effective protection against hate speech. Nevertheless, the phenomenon in question must be constantly analysed in the context of contemporary social and civilisation challenges so that the legal system in Poland responds to these challenges.

The eradication of hate speech on social media: a systematic review

PurposeThe purpose of this paper is to present a quantitative and qualitative synthesis of the diverse academic proposals and initiatives for preventing and eliminating hate speech on the internet.Design/methodology/approachThe foundation for this study is a systematic review of papers devoted to the analysis of hate speech. It has been conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol and applied to an initial corpus of 436 academic texts. Having implemented the suitability, screening and inclusion criteria, this corpus was refined to a sample of 74 articles.FindingsThe main subject categories studied in this corpus of academic research are legal issues and social media. In the majority of the articles, the use of hate speech via social media is associated with five typologies: religion, cyber racism, political slurs, misogyny and attacks on the LGTBI community. The absence of ethical reflection is one of the major shortcomings of IT-focused research and analysis devoted to online hate speech.Practical implicationsTo date various systematic reviews have been presented, and they focus on detecting or describing hate speech. These have used either the search appraisal synthesis analysis framework or the Cochrane network. The PRISMA protocol was applied for this study, and both Scopus and texts in German were included. To date no major, rigorous systematic review has been undertaken of proposals to combat hate speech.Originality/valueThe link between hate speech and poverty has not been studied in depth within the academic sphere. Tolerance and ethical compassion are not granted the attention they merit when it comes to analysing the phenomenon of hate speech.

A corpus-assisted critical discourse analysis of hate speech in German and Polish social media posts

The present paper investigates linguistic data that include hate speech motivated by the alleged or real ethnic or national identity of its addressee in German and Polish Internet-based communication. Focusing on hate speech on Facebook, this paper contributes to the studies of hate speech (e.g. Meibauer, 2013; Meibauer, 2021) in online communication (e.g. Baider & Constantinou, 2020; He et al., 2021; Founta & Specia, 2021) and, in a more general sense, to the vast area of research on the use of aggressive language. Based on a representative corpus, the predominant categories of user-generated hateful statements are teased out relative to their form and stance by means of critical discourse analysis and corpus linguistics tools. In the analytical part, we discuss the discursive practices used in the posts, together with the meanings they communicate, followed by a comparative analysis of their frequencies in comparable corpora. Our findings confirm that hate speech is linguistically conditioned by its socio-cultural context. For instance, our contrastive analysis of German and Polish online data indicates that the two nationalities use different discursive practices to express their aversion to the Other, and that Polish comments are more overtly insulting than German comments.

Discourse recognition and analysis of hate speech in Virginia Woolf's “Mrs Dalloway”

Hate speech is a notion that many people see as intuitively easy to grasp, while many others deny it is even an understandable concept. The present article aims to identify and examine the hate speech situations manifested in Virginia Woolf’s famous novel, “Mrs. Dalloway”. An alternative reading of the novel “Mrs. Dalloway” explores the seemingly minor character, but with a substantial impact on the unfolding of the plotline, Miss Kilman represents a secondary objective. Following the triangle, Clarissa Dalloway - her daughter, Elizabeth, and Doris Kilman, the present essay tries to explain the situations in which hate speech occurs in “Mrs. Dalloway”.

Demonising Migrants in Contexts of Extremism: Analysis of Hate Speech in UK and Spain

Hate speech has been seen as a problem within democratic societies that has been exacerbated by social media. While platforms claim to moderate content, this proves impossible. Studying popular platforms in the UK and Spain and examining content within community pages dedicated to right-wing parties, we use framing analysis to identify the predominant frames in user comments that contained hate speech against migrants. Our research demonstrates a frequent use of arguments that encourage xenophobic and discriminatory attitudes to flourish. Specifically, we find that immigrants are commonly framed as potential criminals, people who steal resources and erode norms of the dominant culture and traditions. The fact that these frames are commonly used is worrying and indicates xenophobic attitudes exist within both societies under study. However, it is difficult to imagine regulatory systems that would prevent these attitudes from being expressed. Rather, we argue that it is more important to focus on correcting the conditions that cause such attitudes to take hold within a society.

“We’re led by stupid people”: Exploring Trump’s use of denigrating and deprecating speech to promote hatred and violence

In response to a call for criminologists to consider the impact of former President Donald Trump’s presumed criminality, we analyze verbal-textual hostility (VTH) in Trump’s campaign speeches. Politicians have particular power and reach with their speech and their use of VTH is an important part of the trifecta of violence. Using a framework informed by linguistic theory and previous analysis of hate speech in recorded hate crimes, we present the categories of deprecation and denigration, and discuss their relationship to domination. In context, these forms of VTH enhance and serve as precursors to more violent speech and acts.