Hidden in plain sight for too long: Using text mining techniques to shine a light on workplace sexism and sexual harassment.

Karami, Amir; Swan, Suzanne C.; White, C. Nicole; Ford, Kayla

doi:10.1037/vio0000239

Cited by 29 publications

(22 citation statements)

References 57 publications

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“…LDA has been applied on both long-length (e.g., abstracts) and short-length (e.g., tweets) corpora for different applications such as health [26], [45]- [47], e-petitions [48], politics [29], [49], analysis of sexual harassment experiences [50], [51], opinion mining [52], investigation of social media strategy [53], [54], SMS spam detection [55], transportation literature [56], and mobile work [57], and literature review surveys relevant to depressive disorder [58], wearable technology [59], biomedical [36], [60], and medical case reports [37].…”

Section: Topic Modelingmentioning

confidence: 99%

Twitter and Research: A Systematic Literature Review Through Text Mining

et al. 2020

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Researchers have collected Twitter data to study a wide range of topics. This growing body of literature, however, has not yet been reviewed systematically to synthesize Twitter-related papers. The existing literature review papers have been limited by constraints of traditional methods to manually select and analyze samples of topically related papers. The goals of this retrospective study are to identify dominant topics of Twitter-based research, summarize the temporal trend of topics, and interpret the evolution of topics withing the last ten years. This study systematically mines a large number of Twitter-based studies to characterize the relevant literature by an efficient and effective approach. This study collected relevant papers from three databases and applied text mining and trend analysis to detect semantic patterns and explore the yearly development of research themes across a decade. We found 38 topics in more than 18,000 manuscripts published between 2006 and 2019. By quantifying temporal trends, this study found that while 23.7% of topics did not show a significant trend (P => 0.05), 21% of topics had increasing trends and 55.3% of topics had decreasing trends that these hot and cold topics represent three categories: application, methodology, and technology. The contributions of this paper can be utilized in the growing field of Twitter-based research and are beneficial to researchers, educators, and publishers. INDEX TERMS Literature review, social media, survey, text mining, topic modeling, Twitter.

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Section: Topic Modelingmentioning

confidence: 99%

Twitter and Research: A Systematic Literature Review Through Text Mining

et al. 2020

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“…We developed a mixed method approach, using both computational and qualitative methods (cf. Karami, Swan, White, & Ford, 2019). The computational approach uses text mining methods that allow researchers to analyze massive datasets by recognizing patterns and uncovering hidden knowledge in a corpus (Conte et al, 2012;Hotho et al, 2005;Karami, 2017;Karami, 2019).…”

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confidence: 99%

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

Karami¹,

White

Ford

et al. 2020

Information Processing & Management

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Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study. We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme. Fourteen percent of the topics were in the gender harassment theme, in which insulting, sexist, or degrading comments or behavior was directed towards women. Five percent of the topics involved sexual coercion (a benefit is offered in exchange for sexual favors), 5% involved sex discrimination, and 7% of the topics discussed retaliation against the victim for reporting the harassment, or for simply not complying with the harasser. Findings highlight the power differential between faculty and students, and the toll on students when professors abuse their power. While some topics did differ based on type of institution, there were no differences between the topics based on gender of harasser or field of study. This research can be beneficial to researchers in further investigation of this paper's dataset, and to policymakers in improving existing policies to create a safe and supportive environment in academia.

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“…We then moved to a qualitative coding process to inductively interpret topics. Our coding was based on reviewing the top related words within each topic using P(W|T) and the top related tweets within each topic using P(T|D) (Karami, Swan, et al, 2019).…”

Section: Topic Analysismentioning

confidence: 99%

Social media and COVID‐19: Characterizing anti‐quarantine comments on Twitter

Karami

Mackenzie

2020

Proc Assoc Inf Sci Technol

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Social media has become a mainstream channel of communication during the COVID‐19 pandemic. While some studies have been developed on investigating public opinion on social media data regarding COVID‐19 pandemic, there is no study analyzing anti‐quarantine comments on social media. This study has collected and analyzed near 80,000 tweets to understand anti‐quarantine social comments. Using text mining, we found 11 topics representing different issues such as comparing COVID‐19 and flu and health side effects of quarantine. We believe that this study shines a light on public opinion of people who are against quarantine.

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Hidden in plain sight for too long: Using text mining techniques to shine a light on workplace sexism and sexual harassment.

Cited by 29 publications

References 57 publications

Twitter and Research: A Systematic Literature Review Through Text Mining

Twitter and Research: A Systematic Literature Review Through Text Mining

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

Social media and COVID‐19: Characterizing anti‐quarantine comments on Twitter

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