Categorizing Online Harassment on Twitter

Saeidi, Mozhgan; Sousa, Samuel; Milios, Evangelos; Zeh, Norbert; Berton, Lilian

doi:10.1007/978-3-030-43887-6_22

Cited by 14 publications

(3 citation statements)

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“…This scoping review examined the current state of using Twitter data for sexual violence research, with a focus on publication year, publication source (ie, journal or conference), research objectives, and ethical considerations surrounding using Twitter data. We identified 7 main objectives after reviewing and summarizing the stated research objectives, including (1) exploring online disclosures and public opinions of sexual violence victimization [20,31]; (2) analyzing Twitter activities and discussions about focusing events or cases related to sexual violence, such as "Wolf pack," "Hawthron case," [32] "New Delhi Gangrape," "Ray Rice," and "Janay Rice" [23]; (3) investigating cultural perceptions of sexual assault [33]; (4) building tools to capture offensive and abusive language on Twitter [34]; (5) using Twitter as a tool to set public agenda and influence policies related to sexual violence, for example, Clark and Evans [35] examined how factors (ie, gender, partisanship, and ideology) influence Congress members' tweet activities about the #MeToo movement; (6) building and testing algorithms to categorize tweets containing harassment [36,37]; and (7) examining public discourses under popular sexual assault-related hashtags (ie, #whyistatyed and #whyididn'treport) [20] or with key terms of "domestic violence" [38]. When using Twitter as a data source, most studies focused on analyzing tweets to examine sexual assault events in the society, such as assessing the reactions of Twitter users as supportive or detractive, as well as exploring the public discourse and personal revelation surrounding sexual violence.…”

Section: Research Objectivesmentioning

confidence: 99%

Using Twitter-Based Data for Sexual Violence Research: Scoping Review

Xue¹,

Zhang²,

Zhang³

et al. 2023

J Med Internet Res

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Background Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study. Objective We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies. Methods We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles. Results We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations. Conclusions We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research.

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Section: Research Objectivesmentioning

confidence: 99%

Using Twitter-Based Data for Sexual Violence Research: Scoping Review

Xue¹,

Zhang²,

Zhang³

et al. 2023

J Med Internet Res

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“…In the NLP domain, deep neural networks have brought groundbreaking results to many tasks in the past few years (Feng et al 2020;Vaswani et al 2018;Duarte et al 2021;Sarikaya et al 2014;Liu et al 2017a;Saeidi et al 2019). Text data has demanded the creation of methods specially designed for its representation, such as word embedding and general-purpose language models.…”

Section: Deep Learning For Natural Language Processingmentioning

confidence: 99%

How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing

Sousa¹,

Kern²

2022

Preprint

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Deep learning (DL) models for natural language processing (NLP) tasks often handle private data, demanding protection against breaches and disclosures. Data protection laws, such as the European Union's General Data Protection Regulation (GDPR), thereby enforce the need for privacy. Although many privacy-preserving NLP methods have been proposed in recent years, no categories to organize them have been introduced yet, making it hard to follow the progress of the literature. To close this gap, this article systematically reviews over sixty DL methods for privacy-preserving NLP published between 2016 and 2020, covering theoretical foundations, privacy-enhancing technologies, and analysis of their suitability for real-world scenarios. First, we introduce a novel taxonomy for classifying the existing methods into three categories: data safeguarding methods, trusted methods, and verification methods. Second, we present an extensive summary of privacy threats, datasets for applications, and metrics for privacy evaluation. Third, throughout the review, we describe privacy issues in the NLP pipeline in a holistic view. Further, we discuss open challenges in privacy-preserving NLP regarding data traceability, computation overhead, dataset size, the prevalence of human biases in embeddings, and the privacy-utility tradeoff. Finally, this review presents future research directions to guide successive research and development of privacy-preserving NLP models.

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“…The impact of social media in a number of areas has been documented at the individual and societal levels: political influence [ 15 , 17 , 20 , 21 ], individuals’ mental health affected by sexist and racist discourse online [ 16 , 22 ], and public health messages [ 19 , 23 , 24 ]. The prevalence of ageist content and the use of unsuitable language are troubling, not just for older adults but for everyone, given that we all age.…”

Section: Introductionmentioning

confidence: 99%

Examining Public Awareness of Ageist Terms on Twitter: Content Analysis

Schramm¹,

Yang²,

Chang³

et al. 2023

JMIR Aging

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Background The World Health Organization, the Centers for Disease Control and Prevention, and the Gerontological Society of America have made efforts to raise awareness on ageist language and propose appropriate terms to denote the older adult population. The COVID-19 pandemic and older adults’ vulnerability to the disease have perpetuated hostile ageist discourse on social media. This is an opportune time to understand the prevalence and use of ageist language and discuss the ways forward. Objective This study aimed to understand the prevalence and situated use of ageist terms on Twitter. Methods We collected 60.32 million tweets between March and July 2020 containing terms related to COVID-19. We then conducted a mixed methods study comprising a content analysis and a descriptive quantitative analysis. Results A total of 58,930 tweets contained the ageist terms “old people” or “elderly.” The more appropriate term “older adult” was found in 11,328 tweets. Twitter users used ageist terms (eg, “old people” and “elderly”) to criticize ageist messages (17/60, 28%), showing a lack of understanding of appropriate terms to describe older adults. Highly hostile ageist content against older adults came from tweets that contained the derogatory terms “old people” (22/30, 73%) or “elderly” (13/30, 43%). Conclusions The public discourse observed on Twitter shows a continued lack of understanding of appropriate terms to use when referring to older adults. Effort is needed to eliminate the perpetuation of ageist messages that challenge healthy aging. Our study highlights the need to inform the public about appropriate language use and ageism.

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Categorizing Online Harassment on Twitter

Cited by 14 publications

References 14 publications

Using Twitter-Based Data for Sexual Violence Research: Scoping Review

Using Twitter-Based Data for Sexual Violence Research: Scoping Review

How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing

Examining Public Awareness of Ageist Terms on Twitter: Content Analysis

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