Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach
- URL: http://arxiv.org/abs/2409.20204v1
- Date: Mon, 30 Sep 2024 11:34:39 GMT
- Title: Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach
- Authors: Aditi Dutta, Susan Banducci, Chico Q. Camargo,
- Abstract summary: We present a semi-automated way to narrow down the search results in the different phases of selection stage in the PRISMA flowchart.
We examine literature from computer science and the social sciences from 2012 to 2022.
We discuss the challenges and opportunities for future research dedicated to measuring online sexism and misogyny.
- Score: 1.1599570446840546
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, several computational tools have been developed to detect and identify sexism, misogyny, and gender-based hate speech, especially on online platforms. Though these tools intend to draw on knowledge from both social science and computer science, little is known about the current state of research in quantifying online sexism or misogyny. Given the growing concern over the discrimination of women in online spaces and the rise in interdisciplinary research on capturing the online manifestation of sexism and misogyny, a systematic literature review on the research practices and their measures is the need of the hour. We make three main contributions: (i) we present a semi-automated way to narrow down the search results in the different phases of selection stage in the PRISMA flowchart; (ii) we perform a systematic literature review of research papers that focus on the quantification and measurement of online gender-based hate speech, examining literature from computer science and the social sciences from 2012 to 2022; and (iii) we identify the opportunities and challenges for measuring gender-based online hate speech. Our findings from topic analysis suggest a disciplinary divide between the themes of research on sexism/misogyny. With evidence-based review, we summarise the different approaches used by the studies who have explored interdisciplinary approaches to bridge the knowledge gap. Coupled with both the existing literature on social science theories and computational modeling, we provide an analysis of the benefits and shortcomings of the methodologies used. Lastly, we discuss the challenges and opportunities for future research dedicated to measuring online sexism and misogyny.
Related papers
- Fairness and Bias Mitigation in Computer Vision: A Survey [61.01658257223365]
Computer vision systems are increasingly being deployed in high-stakes real-world applications.
There is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data.
This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision.
arXiv Detail & Related papers (2024-08-05T13:44:22Z) - A multitask learning framework for leveraging subjectivity of annotators to identify misogyny [47.175010006458436]
We propose a multitask learning approach to enhance the performance of the misogyny identification systems.
We incorporated diverse perspectives from annotators in our model design, considering gender and age across six profile groups.
This research advances content moderation and highlights the importance of embracing diverse perspectives to build effective online moderation systems.
arXiv Detail & Related papers (2024-06-22T15:06:08Z) - Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
Ontologies are widely used for representing domain knowledge and meta data.
One straightforward solution is to integrate statistical analysis and machine learning.
Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
arXiv Detail & Related papers (2024-06-16T14:49:19Z) - Who benefits from altmetrics? The effect of team gender composition on
the link between online visibility and citation impact [0.0]
Women's articles receive fewer academic citations than men's.
Online visibility positively affects citations across research areas.
Team gender composition interacts differently with visibility in these research areas.
arXiv Detail & Related papers (2023-08-01T09:33:31Z) - Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and
Addressing Sociological Implications [0.0]
The study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge.
The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of Large Language Models.
The paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques.
arXiv Detail & Related papers (2023-07-18T11:38:45Z) - Tainted Love: A Systematic Review of Online Romance Fraud [68.8204255655161]
Romance fraud involves cybercriminals engineering a romantic relationship on online dating platforms.
We characterise the literary landscape on romance fraud, advancing the understanding of researchers and practitioners.
Three main contributions were identified: profiles of romance scams, countermeasures for mitigating romance scams, and factors that predispose an individual to become a scammer or a victim.
arXiv Detail & Related papers (2023-02-28T20:34:07Z) - Aggression and "hate speech" in communication of media users: analysis
of control capabilities [50.591267188664666]
Authors studied the possibilities of mutual influence of users in new media.
They found a high level of aggression and hate speech when discussing an urgent social problem - measures for COVID-19 fighting.
Results can be useful for developing media content in a modern digital environment.
arXiv Detail & Related papers (2022-08-25T15:53:32Z) - Sex and Gender in the Computer Graphics Research Literature [4.05984965639419]
We survey the treatment of sex and gender in the Computer Graphics research literature from an algorithmic fairness perspective.
The established practices on the use of gender and sex in our community are scientifically incorrect and constitute a form of algorithmic bias with potential harmful effects.
arXiv Detail & Related papers (2022-06-01T13:24:17Z) - For Better or for Worse? A Framework for Critical Analysis of ICT4D for
Women [0.0]
As ICT diffusion widens, there is a persistent threat of widening the gender-based digital divide.
This paper develops a critical research framework for a gender-focused examination of ICT4D studies.
arXiv Detail & Related papers (2021-08-23T05:42:24Z) - Gender bias in magazines oriented to men and women: a computational
approach [58.720142291102135]
We compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group over a decade.
With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time.
Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time.
arXiv Detail & Related papers (2020-11-24T14:02:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.