Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults

Pandey, Rahul; Purohit, Hemant; Stabile, Bonnie

doi:10.1109/wi.2018.00-80

Cited by 15 publications

(19 citation statements)

References 20 publications

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“…Thus, the data availability of social media has helped in many recent works related to GBV, which have delivered a multitude of interesting findings ( Moitra, Ahmed & Chandra, 2021 ; Razi, 2020 ; Pandey et al, 2018 ; Khatua, Cambria & Khatua, 2018 ). Moreover, location-tagged social media data also assist in several cross-cultural studies related to GBV ( Purohit et al, 2015 ; Starkey et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

Is gender-based violence a confluence of culture? Empirical evidence from social media

Rimjhim

Dandapat

2022

PeerJ Computer Science

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Gender-based violence (GBV) has been plaguing our society for long back. The severity of GBV has spurred research around understanding the causes and factors leading to GBV. Understanding factors and causes leading to GBV is helpful in planning and executing efficient policies to curb GBV. Past researches have claimed a country’s culture to be one of the driving reasons behind GBV. The culture of a country consists of cultural norms, societal rules, gender-based stereotypes, and social taboos which provoke GBV. These claims are supported by theoretical or small-scale survey-based research that suffers from under-representation and biases. With the advent of social media and, more importantly, location-tagged social media, huge ethnographic data are available, creating a platform for many sociological research. In this article, we also utilize huge social media data to verify the claim of confluence between GBV and the culture of a country. We first curate GBV content from different countries by collecting a large amount of data from Twitter. In order to explore the relationship between a country’s culture and GBV content, we performed correlation analyses between a country’s culture and its GBV content. The correlation results are further re-validated using graph-based methods. Through the findings of this research, we observed that countries with similar cultures also show similarity in GBV content, thus reconfirming the relationship between GBV and culture.

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Section: Related Workmentioning

confidence: 99%

Is gender-based violence a confluence of culture? Empirical evidence from social media

Rimjhim

Dandapat

2022

PeerJ Computer Science

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“…Pandey et al [67] developed an approach for categorization of intentions. Their approach was based on a linear model of logistic regression and CNN in addition to being supported by distributional semantic.…”

Section: Machine Learning and Mixed Learning Based Solutionsmentioning

confidence: 99%

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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“…• (Pandey et al 2018) created a dataset of 2,500 tweets for identification of malicious intent surrounding the cases of sexual assault. The tweets were annotated for labels like accusational, validation, sensational.…”

Section: Related Datasetsmentioning

confidence: 99%

#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement

Gautam¹,

Mathur²,

Gosangi³

et al. 2019

Preprint

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In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.

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Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults

Cited by 15 publications

References 20 publications

Is gender-based violence a confluence of culture? Empirical evidence from social media

Is gender-based violence a confluence of culture? Empirical evidence from social media

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement

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