This paper presents research uncovering systematic gender bias in the representation of political leaders in the media, using artificial intelligence. Newspaper coverage of Irish ministers over a fifteen year period was gathered and analysed with natural language processing techniques and machine learning. Findings demonstrate evidence of gender bias in the portrayal of female politicians, the kind of policies they were associated with and how they were evaluated in terms of their performance as political leaders. This paper also sets out a methodology whereby media content may be analysed on a large scale utilising techniques from artificial intelligence within a theoretical framework founded in gender theory and feminist linguistics.
UNC Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；University of British Columbia（不列颠哥伦比亚大学）
Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly ("women can't park") or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist's motivations, attributes, mental states, and implications on others. Our findings regar…
SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models
School of Information Science, Beijing Language and Culture University（北京语言大学信息科学学院）；Libraries, Renmin University of China（中国人民大学图书馆）；School of Humanities, Tsinghua University（清华大学人文学院）；Shanghai University of Finance and Economics（上海财经大学）；National Print Media Language Resources Monitoring & Research Center（国家印刷媒体语言资源监测与研究中心）
Despite their prevalence in society, social biases are difficult to identify, primarily because human judgements in this domain can be unreliable. We take an unsupervised approach to identifying gender bias against women at a comment level and present a model that can surface text likely to contain bias. Our main challenge is forcing the model to focus on signs of implicit bias, rather than other artifacts in the data. Thus, our methodology involves reducing the influence of confounds through propensity matching and adversarial learning. Our analysis shows how biased comments directed towards …
Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns
Department of Computer Science, Aarhus University（阿arhus大学计算机科学系）；AMD Silo AI；University of Tokyo（东京大学）；IT University of Copenhagen（哥本哈根IT大学）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）
The media attention to the personal sphere of famous and important individuals has become a key element of the gender narrative. Here we combine lexical, syntactic and sentiment analysis to investigate the role of gender in the personalization of a wide range of political office holders in Italy during the period 2017-2020. On the basis of a score for words that is introduced to account for gender unbalance in both representative and news coverage, we show that the political personalization in Italy is more detrimental for women than men, with the persistence of entrenched stereotypes includin…
Social media is commonly used by the public during election campaigns to express their opinions regarding different issues. Among various social media channels, Twitter provides an efficient platform for researchers and politicians to explore public opinion regarding a wide range of topics such as the economy and foreign policy. Current literature mainly focuses on analyzing the content of tweets without considering the gender of users. This research collects and analyzes a large number of tweets and uses computational, human coding, and statistical analyses to identify topics in more than 300…
LMU Munich（慕尼黑大学）；Bloomberg（彭博）；University of Sheffield（谢菲尔德大学）
Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts. In this paper, we present the first computational study of parody. We introduce a new publicly available data set of tweets from real politicians and their corresponding parody accounts. We run a battery of supervised machine learning models for automatically detecting parody tweets with an emphasis on robustness by testing on tweets from accounts unseen in training, across different genders and across countries. O…
College of Information and Communications（信息与传播学院）；University of South Carolina（南卡罗来纳大学）；University of Arkansas（阿肯色大学）
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.…
High Risk of Political Bias in Black Box Emotion Inference Models
Institute of Psychology, Polish Academy of Sciences（波兰科学院心理学研究所）；Stowarzyszenie Otwartej Nauki（开放科学协会）；Institute of Philosophy and Sociology, Polish Academy of Sciences（波兰科学院哲学与社会学研究所）
