Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining. Issue 2 (March 2020)
- Record Type:
- Journal Article
- Title:
- Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining. Issue 2 (March 2020)
- Main Title:
- Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining
- Authors:
- Karami, Amir
White, Cynthia Nicole
Ford, Kayla
Swan, Suzanne
Yildiz Spinel, Melek - Abstract:
- Highlights: This paper provides a dataset containing more than 2000 sexual harassment experiences in academia. A computational approach was utilized to overcome the time–‐consuming and labor–‐intensive process of traditional methods. This research detected, analyzed, and categorized the topics of the sexual harassment experiences in academia. Results are beneficial to researchers interested in further investigation of this paper's dataset. Findings have utility for policymakers in improving existing policies to create a safe and supportive environment in academia. Abstract: 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 wasHighlights: This paper provides a dataset containing more than 2000 sexual harassment experiences in academia. A computational approach was utilized to overcome the time–‐consuming and labor–‐intensive process of traditional methods. This research detected, analyzed, and categorized the topics of the sexual harassment experiences in academia. Results are beneficial to researchers interested in further investigation of this paper's dataset. Findings have utility for policymakers in improving existing policies to create a safe and supportive environment in academia. Abstract: 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. … (more)
- Is Part Of:
- Information processing & management. Volume 57:Issue 2(2020:Mar.)
- Journal:
- Information processing & management
- Issue:
- Volume 57:Issue 2(2020:Mar.)
- Issue Display:
- Volume 57, Issue 2 (2020)
- Year:
- 2020
- Volume:
- 57
- Issue:
- 2
- Issue Sort Value:
- 2020-0057-0002-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-03
- Subjects:
- Sexual harassment -- Web survey -- Text mining -- Academia -- Topic modeling
Information storage and retrieval systems -- Periodicals
Information science -- Periodicals
Systèmes d'information -- Périodiques
Sciences de l'information -- Périodiques
Information science
Information storage and retrieval systems
Periodicals
658.4038 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064573 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ipm.2019.102167 ↗
- Languages:
- English
- ISSNs:
- 0306-4573
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4493.893000
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British Library HMNTS - ELD Digital store - Ingest File:
- 12552.xml