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

Sexual Harassment in the Academy: Harnessing the Growing Labor Movement in Higher Education to Address Sexual Harassment Against Graduate Workers

Higher education is not immune to the epidemic of sexual harassment in the United States, particularly sexual harassment of graduate workers. This is due largely to power differentials of status and income, as academia relies on low-wage work. While the literature shows sexual harassment is prevalent across disciplines, current work to address the problem does not account for graduate worker precarity. The graduate labor movement, which addresses precarity, is beginning to tackle sexual harassment. We review how the labor and anti-gender-based violence movements in higher education should come together to prevent sexual harassment, presenting recommendations for structural changes to academia.

Investigating diseases and chemicals in COVID-19 literature with text mining

Given the rapidly unfolding nature of the COVID-19 pandemic, there is an urgent need to streamline the literature synthesis of the growing scientific research to elucidate targeted solutions. Traditional systematic literature review studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, and lack of data-driven tools. This research has collected 9298 papers representing COVID-19 research published through May 5, 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling that provided 25 categories showing associations between the two overarching categories. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

Twenty years of gender equality research: A scoping review based on a new semantic indicator

Gender equality is a major problem that places women at a disadvantage thereby stymieing economic growth and societal advancement. In the last two decades, extensive research has been conducted on gender related issues, studying both their antecedents and consequences. However, existing literature reviews fail to provide a comprehensive and clear picture of what has been studied so far, which could guide scholars in their future research. Our paper offers a scoping review of a large portion of the research that has been published over the last 22 years, on gender equality and related issues, with a specific focus on business and economics studies. Combining innovative methods drawn from both network analysis and text mining, we provide a synthesis of 15,465 scientific articles. We identify 27 main research topics, we measure their relevance from a semantic point of view and the relationships among them, highlighting the importance of each topic in the overall gender discourse. We find that prominent research topics mostly relate to women in the workforce-e.g., concerning compensation, role, education, decision-making and career progression. However, some of them are losing momentum, and some other research trends-for example related to female entrepreneurship, leadership and participation in the board of directors-are on the rise. Besides introducing a novel methodology to review broad literature streams, our paper offers a map of the main gender-research trends and presents the most popular and the emerging themes, as well as their intersections, outlining important avenues for future research.

A Systematic Literature Review of Sexual Harassment Studies with Text Mining

Sexual harassment has been the topic of thousands of research articles in the 20th and 21st centuries. Several review papers have been developed to synthesize the literature about sexual harassment. While traditional literature review studies provide valuable insights, these studies have some limitations including analyzing a limited number of papers, being time-consuming and labor-intensive, focusing on a few topics, and lacking temporal trend analysis. To address these limitations, this paper employs both computational and qualitative approaches to identify major research topics, explore temporal trends of sexual harassment topics over the past few decades, and point to future possible directions in sexual harassment studies. We collected 5320 research papers published between 1977 and 2020, identified and analyzed sexual harassment topics, and explored the temporal trend of topics. Our findings indicate that sexual harassment in the workplace was the most popular research theme, and sexual harassment was investigated in a wide range of spaces ranging from school to military settings. Our analysis shows that 62.5% of the topics having a significant trend had an increasing (hot) temporal trend that is expected to be studied more in the coming years. This study offers a bird’s eye view to better understand sexual harassment literature with text mining, qualitative, and temporal trend analysis methods. This research could be beneficial to researchers, educators, publishers, and policymakers by providing a broad overview of the sexual harassment field.

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population.

Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter

To combat health disinformation shared online, there is a need to identify and characterize the prevalence of topics shared by trolls managed by individuals to promote discord. The current literature is limited to a few health topics and dominated by vaccination. The goal of this study is to identify and analyze the breadth of health topics discussed by left (liberal) and right (conservative) Russian trolls on Twitter. We introduce an automated framework based on mixed methods including both computational and qualitative techniques. Results suggest that Russian trolls discussed 48 health-related topics, ranging from diet to abortion. Out of the 48 topics, there was a significant difference (p-value ≤ 0.004) between left and right trolls based on 17 topics. Hillary Clinton's health during the 2016 election was the most popular topic for right trolls, who discussed this topic significantly more than left trolls. Mental health was the most popular topic for left trolls, who discussed this topic significantly more than right trolls. This study shows that health disinformation is a global public health threat on social media for a considerable number of health topics. This study can be beneficial for researchers who are interested in political disinformation and health monitoring, communication, and promotion on social media by showing health information shared by Russian trolls.

"Having more women humanitarian leaders will help transform the humanitarian system": challenges and opportunities for women leaders in conflict and humanitarian health

BACKGROUND

It is estimated that over 40% of the half a million humanitarian workers who provide frontline care during emergencies, wars and disasters, are women. Women are at the forefront of improving health for conflict-affected populations through service delivery, education and capacity strengthening, advocacy and research. Women are also disproportionately affected by conflict and humanitarian emergencies. The growing evidence base demonstrating excess female morbidity and mortality reflects the necessity of evaluating the role of women in leadership driving health research, policy and programmatic interventions in conflict-related humanitarian contexts. Despite global commitments to improving gender equality, the issue of women leaders in conflict and humanitarian health has been given little or no attention. The aim of this paper focuses on three domains: importance, barriers and opportunities for women leaders in conflict and humanitarian health. Following thematic analysis of the material collected, we discuss the following themes: barriers of women's leadership domain at societal level, and organisational level, which is subcategorized into culture and strategy. Building on the available opportunities and initiatives and on inspirational experiences of the limited number of women leaders in this field, recommendations for empowering and supporting women's leadership in conflict health are presented.

METHODS

A desk-based literature review of academic and grey sources was conducted followed by thematic analysis.

RESULTS

There is very limited evidence on women leaders in conflict and humanitarian health. Some data shows that women have leadership skills that help to support more inclusive solutions which are incredibly important in this sector. However, deeply imbedded discrimination against women at the organisational, cultural, social, financial and political levels is exacerbated in conflict which makes it more challenging for women to progress in such settings.

CONCLUSION

Advocating for women leaders in conflict and health in the humanitarian sector, governmental bodies, academia and the global health community is crucial to increasing effective interventions that adequately address the complexity and diversity of humanitarian crises.

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

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.

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e., the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets' structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals' opinions at the beginning of posts. "Gender" and "gap" elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science "sacrificing personal skills for success". The semantic frame of "man" highlighted awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that tweets got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. This analysis thus identified that Twitter discourse mostly starting conversations promoted a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users' replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.

Exploring research trends in big data across disciplines: A text mining analysis

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several topics associated with the storage, collection and analysis of large datasets; the publications were predominantly published in computational fields. Other identified research topics show the influence of big data methods and techniques in areas beyond computer science, such as education, urban informatics, business, health and medical sciences. In fact, the prevalence of these topics has increased over time. In contrast, some themes like parallel computing, network modeling and big data analytic techniques have lost their popularity in recent years. These results probably reflect the maturity of big data core topics and highlight flourishing new research trends pertinent to big data in new domains, especially in social sciences, health and medicine. Findings of this article can be beneficial for researchers and science policymakers to understand the scope and structure of big data in different academic disciplines.