Natural language model for automatic identification of Intimate Partner Violence reports from Twitter

Al-Garadi, Mohammed Ali; Kim, Sangmi; Guo, Yuting; Warren, Elise; Yang, Yuan-Chi; Lakamana, Sahithi; Sarker, Abeed

doi:10.1016/j.array.2022.100217

Cited by 17 publications

(6 citation statements)

References 23 publications

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“…In this regard, IPV-related subreddits will be greatly beneficial to learning about survivors’ lived experiences and needs and offering tailored support in a timely manner. Secondly, during the pandemic, ~11% of the IPV-related tweets contained first-hand experiences of IPV ( Al-Garadi et al, 2022 ; Rai et al, 2022 ), whereas one-third of the OPs across the four IPV-related subreddits came from survivors in our study. This figure is also higher than that from surveys.…”

Section: Discussionmentioning

confidence: 68%

Characteristics of Intimate Partner Violence and Survivor’s Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence

Kim

Warren

Jahangir

et al. 2023

J Interpers Violence

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Intimate partner violence (IPV) increased during the COVID-19 pandemic. Collecting actionable IPV-related data from conventional sources (e.g., medical records) was challenging during the pandemic, generating a need to obtain relevant data from non-conventional sources, such as social media. Social media, like Reddit, is a preferred medium of communication for IPV survivors to share their experiences and seek support with protected anonymity. Nevertheless, the scope of available IPV-related data on social media is rarely documented. Thus, we examined the availability of IPV-related information on Reddit and the characteristics of the reported IPV during the pandemic. Using natural language processing, we collected publicly available Reddit data from four IPV-related subreddits between January 1, 2020 and March 31, 2021. Of 4,000 collected posts, we randomly sampled 300 posts for analysis. Three individuals on the research team independently coded the data and resolved the coding discrepancies through discussions. We adopted quantitative content analysis and calculated the frequency of the identified codes. 36% of the posts ( n = 108) constituted self-reported IPV by survivors, of which 40% regarded current/ongoing IPV, and 14% contained help-seeking messages. A majority of the survivors’ posts reflected psychological aggression, followed by physical violence. Notably, 61.4% of the psychological aggression involved expressive aggression, followed by gaslighting (54.3%) and coercive control (44.3%). Survivors’ top three needs during the pandemic were hearing similar experiences, legal advice, and validating their feelings/reactions/thoughts/actions. Albeit limited, data from bystanders (survivors’ friends, family, or neighbors) were also available. Rich data reflecting IPV survivors’ lived experiences were available on Reddit. Such information will be useful for IPV surveillance, prevention, and intervention.

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Section: Discussionmentioning

confidence: 68%

Characteristics of Intimate Partner Violence and Survivor’s Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence

Kim

Warren

Jahangir

et al. 2023

J Interpers Violence

Self Cite

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“…Por ejemplo, Aragón et al [29] y Frenda et al [30], emplearon algoritmos como CNN (Convolutional Neural Network) y LSTM (Long-Short Term Memory), para detectar discursos de odio y agresión en tuits mexicanos (clasificándolos en agresivos y no agresivos), obteniendo resultados por debajo del 60% de exactitud. Por otro lado, Algaradi et al [31] estudiaron varios algoritmos de aprendizaje automático y profundo, incluyendo los actuales transformers, para identificar automáticamente si se denuncia la violencia de pareja o no, en X, logrando resultados entre el 89% y el 95% de exactitud.…”

Section: Trabajos Relacionadosunclassified

“…Sin embargo, se pueden discutir algunas de ellas, por ejemplo, en el trabajo de Prieto Cruz y Montoya Vasquez [12], el tamaño de la clase MV predomina sobre la clase MNV, lo que resulta en tasas de clasificación sobre la clase de interés por encima del 90% en términos de sensibilidad. Otro ejemplo, es el presentado por Al-Garadi et al [31], que muestra valores de exactitud entre 85% y 95%, pero un desempeño en la clase minoritaria (con puntaje F1) de 0.76 y un conjunto de datos que incluye solo 7016 tuits, i.e., el desempeño en la clase principal es menor que el obtenido por nuestro método propuesto. En los trabajos de Salehi et al [13], Frenda et al [30] y Díaz et al [33] el tamaño del conjunto de datos también es menor (el de mayor tamaño tiene 11,000 comentarios aproximadamente).…”

Section: Rendimiento Del Clasificadorunclassified

Detection of violent speech against women in Mexican tweets using an active learning approach

Miranda-Piña,

Alejo,

Rendón-Lara

et al. 2024

IEEE Latin Am. Trans.

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In Latin American and Caribbean States, the verbal violence against women on social networks, such as X (formerly known as Twitter), is a serious threat that has been addressed through the implementation of social norms, public policies, and social movements. Nevertheless, a challenge is the effective and automatic real-time detection of violent tweets. In this sense, traditional machine learning algorithms have been proposed to tackle social issues where the training process is performed in a static manner. However, considering that X is a dynamic environment where a vast number of tweets are generated each second, it requires powerful machine learning algorithms that could exploit this pool of unlabeled data to be incorporated into the model through continuous updates. This paper explores an active learning method based on uncertainty sampling, which identifies the most confusing tweets to be labeled by an expert in real-time. This focused selection prioritizes which data can be used to train a multilayer perceptron that can achieve a better performance with fewer training samples. Experimental results show that including new samples yields promising results, increasing the AUC from 0.8712 to 0.8833.

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“…Among the wide range of AI algorithms, some literature sources compare different conventional machine learning models such as Support Vector Machine (SVM), k-Nearest Neighbours, Decision Tree, and Naive Bayes, in relation to their effectiveness in classifying online posts related to domestic violence [13][14][15]. In the classification of domestic violence-related online content, SVM has demonstrated a general superiority over other traditional machine learning classifiers [16].…”

Section: And Andmentioning

confidence: 99%

Technological Innovations for Tackling Domestic Violence

Kouzani

2023

IEEE Access

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Domestic violence is an issue of great importance that transcends socioeconomic backgrounds and cultural boundaries. It stems from factors such as power struggle, mental health issue, financial hardship, substance abuse, among others. Technology can play a vital role in not only detecting and mitigating instances of domestic violence, but also providing essential support and resources to its victims. This review paper examines the latest technological innovations in tackling domestic violence. It describes a range of technology platforms and tools, and discusses their capabilities and shortcomings. Firstly, the review methodology is given including aims and objectives of the review, search strategy, and selection of sources. Next, technological innovations in the context of domestic violence are defined. Then, the paper presents technological approaches developed to tackle domestic violence through: (i) analysis of data shared through digital platforms is presented, (ii) analysis of data captured by ambient sensors, (iii) analysis of data captured by smartphones, (iv) analysis of data captured by wearable sensors, (v) protecting online activities, (vi) preventing non-contact harassment, (vii) anti-stalking and monitoring measures, and (viii) virtual reality approaches. Next, discussions on the capabilities and applications of these technological innovations across different aspects of domestic violence are presented. The paper also addresses the challenges and limitations associated with these innovations, and gives future directions and recommendations for further research and development.

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Natural language model for automatic identification of Intimate Partner Violence reports from Twitter

Cited by 17 publications

References 23 publications

Characteristics of Intimate Partner Violence and Survivor’s Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence

Characteristics of Intimate Partner Violence and Survivor’s Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence

Detection of violent speech against women in Mexican tweets using an active learning approach

Technological Innovations for Tackling Domestic Violence

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