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10 pages, 821 KiB

Open AccessArticle

Using Twitter to Detect Hate Crimes and Their Motivations: The HateMotiv Corpus

by Noha Alnazzawi

Data 2022, 7(6), 69; https://doi.org/10.3390/data7060069 - 24 May 2022

Cited by 5 | Viewed by 4322

With the rapidly increasing use of social media platforms, much of our lives is spent online. Despite the great advantages of using social media, unfortunately, the spread of hate, cyberbullying, harassment, and trolling can be very common online. Many extremists use social media platforms to communicate their messages of hatred and spread violence, which may result in serious psychological consequences and even contribute to real-world violence. Thus, the aim of this research was to build the HateMotiv corpus, a freely available dataset that is annotated for types of hate crimes and the motivation behind committing them. The dataset was developed using Twitter as an example of social media platforms and could provide the research community with a very unique, novel, and reliable dataset. The dataset is unique as a consequence of its topic-specific nature and its detailed annotation. The corpus was annotated by two annotators who are experts in annotation based on unified guidelines, so they were able to produce an annotation of a high standard with F-scores for the agreement rate as high as 0.66 and 0.71 for type and motivation labels of hate crimes, respectively. Full article

(This article belongs to the Special Issue Knowledge Extraction from Data Using Machine Learning)

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11 pages, 692 KiB

Open AccessArticle

The #StopAsianHate Movement on Twitter: A Qualitative Descriptive Study

by Jiepin Cao, Chiyoung Lee, Wenyang Sun and Jennie C. De Gagne

Int. J. Environ. Res. Public Health 2022, 19(7), 3757; https://doi.org/10.3390/ijerph19073757 - 22 Mar 2022

Cited by 18 | Viewed by 7346

Abstract

Evidence-based intervention and policy strategies to address the recent surge of race-motivated hate crimes and other forms of racism against Asian Americans are essential; however, such efforts have been impeded by a lack of empirical knowledge, e.g., about racism, specifically aimed at the Asian American population. Our qualitative descriptive study sought to fill this gap by using a data-mining approach to examine the contents of tweets having the hashtag #StopAsianHate. We collected tweets during a two-week time frame starting on 20 May 2021, when President Joe Biden signed the COVID-19 Hate Crimes Act. Screening of the 31,665 tweets collected revealed that a total of 904 tweets were eligible for thematic analysis. Our analysis revealed five themes: “Asian hate is not new”, “Address the harm of racism”, “Get involved in #StopAsianHate”, “Appreciate the Asian American and Pacific Islander (AAPI) community’s culture, history, and contributions” and “Increase the visibility of the AAPI community.” Lessons learned from our findings can serve as a foundation for evidence-based strategies to address racism against Asian Americans both locally and globally. Full article

(This article belongs to the Special Issue Big Data for Public Health Research and Practice)

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13 pages, 809 KiB

Open AccessArticle

Evolution of Legislation and Crimes Based on Sexual Identity or Orientation in Spain: A Retrospective Observational Study (2011–2021)

by Laura Ruiz-Azcona, Amada Pellico-López, Jimena B. Manjón-Rodríguez, Mar Sánchez Movellán, Purificación Ajo Bolado, José García-Vázquez, Ildefonso Hernández-Aguado, Joaquín Cayón-De las Cuevas and María Paz-Zulueta

Int. J. Environ. Res. Public Health 2022, 19(2), 859; https://doi.org/10.3390/ijerph19020859 - 13 Jan 2022

Cited by 1 | Viewed by 2132

Abstract

Respect for different sexual options and orientations prevents the occurrence of hate crimes against lesbian, gay, bisexual, trans and intersex (LGTBI) persons for this reason. Our aim was to review the legislation that protects the rights of LGTBI people and to quantify the victimization rates of hate crimes based on sexual identity and orientation. A retrospective observational study was conducted across all regions of Spain from 2011–2021. The laws on LGTBI rights in each region were identified. Hate crime victimization data on sexual identity and orientation were collected in annual rates per 100,000 inhabitants, annual percentage change and average change during the study period to assess the trend. The regulatory development of laws against discrimination against LGTBI individuals is heterogeneous across regions. Overall, in Spain there is an upward trend in the number of hate crime victimizations motivated by sexual identity or orientation. The effectiveness of data collection, thanks to better training and awareness of police forces regarding hate crimes and the processes of data cleansing and consolidation contributes to a greater visibility of hate crimes against LGTBI people. Full article

(This article belongs to the Collection Nursing Research)

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13 pages, 987 KiB

Open AccessArticle

Using Shallow and Deep Learning to Automatically Detect Hate Motivated by Gender and Sexual Orientation on Twitter in Spanish

by Carlos Arcila-Calderón, Javier J. Amores, Patricia Sánchez-Holgado and David Blanco-Herrero

Multimodal Technol. Interact. 2021, 5(10), 63; https://doi.org/10.3390/mti5100063 - 13 Oct 2021

Cited by 17 | Viewed by 4740

Abstract

The increasing phenomenon of “cyberhate” is concerning because of the potential social implications of this form of verbal violence, which is aimed at already-stigmatized social groups. According to information collected by the Ministry of the Interior of Spain, the category of sexual orientation and gender identity is subject to the third-highest number of registered hate crimes, ranking behind racism/xenophobia and ideology. However, most of the existing computational approaches to online hate detection simultaneously attempt to address all types of discrimination, leading to weaker prototype performances. These approaches focus on other reasons for hate—primarily racism and xenophobia—and usually focus on English messages. Furthermore, few detection models have used manually generated databases as a training corpus. Using supervised machine learning techniques, the present research sought to overcome these limitations by developing and evaluating an automatic detector of hate speech motivated by gender and sexual orientation. The focus was Spanish-language posts on Twitter. For this purpose, eight predictive models were developed from an ad hoc generated training corpus, using shallow modeling and deep learning. The evaluation metrics showed that the deep learning algorithm performed significantly better than the shallow modeling algorithms, and logistic regression yielded the best performance of the shallow algorithms. Full article

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