Do men and women respond differently to acts of sexism? According to a survey, ** percent of females and ** percent of males would tell off family or friends making a sexist comment. Furthermore, ** percent of females would confront a man who is harassing a woman in a public place, compared to ** percent males.

In the United States, the majority of the surveyed female tech professionals (** percent) stated that they have experienced gender discrimination in the tech workplace, whereas only (** percent) of men have felt discriminated against in the workplace in 2020. This significant difference between percentages of men and women who have experienced gender discrimination in the tech world highlights a major gender imbalance.

Over half of the surveyed women stated that they have witnessed gender discrimination in the tech industry in regards to salary and benefits, whereas only ** percent of men witnessed the same in 2020. This significant difference between percentages of men and women who have witnessed gender discrimination in the tech world highlights a major gender imbalance.

This dataset contains the results of a three month audit of housing advertisements. It accompanies the 2020 ICWSM paper "Auditing Race and Gender Discrimination in Online Housing Markets". It covers data collected between Dec 7, 2018 and March 19, 2019. There are two json files in the dataset: The first contains a list of json objects representing advertisements separated by newlines. Each object includes the date and time it was collected, the image and title (if collected) of the ad, the page on which it was displayed, and the training treatment it received. The second file is a list of json objects representing a visit to a housing lister separated by newlines. Each object contains the url, training treatment applied, the location searched, and the metadata of the top sites scraped. This metadata includes location, price, and number of rooms. The dataset also includes the raw images of ads collected in order to code them by interest and targeting. These were captured by selenium and named using a perceptive hash to de-duplicate images.

Nearly half of female journalists surveyed in 2020 stated that they would censor themselves as a result of gender-based violence. A report looking at the consequences of sexism on journalists found that 37 percent of female journalists worldwide suffered with less motivation following sexist abuse at work, and 13 percent did not have their contract renewed or were fired.

To what extent do sexism-related views influence Americans' voting behavior? Gender-related issues are increasingly salient, but whether they will consolidate into a durable cleavage hinges on their relationship with pre-existing divides like race. Prior work has frequently considered racial and gender divisions separately, leaving questions about their interplay and differences unanswered. Employing a novel two-wave panel design in 2019-2020, we examine how cross-pressured respondents make trade-offs when they agree with candidate statements on one dimension but not the other. We find that gender progressives sometimes prioritize issue fit on gender. However, respondents holding sexist views rarely reward candidates espousing those same views, incentivizing most candidates to avoid such stances. By contrast, respondents penalize candidates disagreeing with them on racially charged issues, results which persist in a 2023 survey. Though respondents hold strong views on gender-related issues, these views do not presently structure political competition to the extent that racial positions do.

This dataset contains the tweet ids of 1,360,764 tweets, including tweets between April 1, 2020 and June 30, 2020. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that there will be no updates to this dataset.The following list of terms includes the hashtags used to collect data for this dataset: #metoo, #timesup, #metoostem, #sciencetoo, #metoophd, #shittymediamen, #churchtoo, #ustoo, #metooMVMT, #ARmetoo, #TimesUpAR, #metooSociology, #metooSexScience, #timesupAcademia, and #metooMedicine.Be aware that previous quarters (up to the first quarter of 2020) only include one hashtag: #metoo.Because of the size of the files, the list of identifiers are split into 2 files containing up to 1,000,000 ids each.Per Twitter's Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets that are still available (not deleted by users) tools like Hydrator are available.There are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available by quarter, as well as a full dataset with a larger corpus of hashtags.

According to a survey conducted in South Korea in 2020, about **** percent of female respondents felt that South Korean society was unfair to women, while only **** percent of male respondents felt the same. On the contrary, about **** percent of men thought they were treated unfairly.

How prevalent is gender bias among U.S. politicians? We analyze the transcripts of every congressional hearing attended by the chair of the U.S. Federal Reserve from 2001 to 2020 to provide a carefully identified effect of sexism, using Janet Yellen as a bundled treatment. We find that legislators who interacted with both Yellen and at least one other male Fed chair over this period interrupt Yellen more, and interact with her using more aggressive tones. Furthermore, we show that the increase in hostility experienced by Yellen relative to her immediate predecessor and successor are absent among those legislators with daughters. Our results point to the important role of societal biases bleeding into seemingly unrelated policy domains, underscoring the vulnerability of democratic accountability and oversight mechanisms to existing gender norms and societal biases.

It is well established that Donald Trump's rhetoric and actions during his candidacy and presidency endorsed existing group-based social hierarchies, helping to boost his support among White Americans, especially men and those without a college degree. But how did these endorsements shape support for Trump among non-White Americans? Extant theories suggest that these actions should have pushed racial and ethnic minority voter support for the GOP candidate to its lowest observed levels in contemporary political history. Yet Trump outperformed these expectations in 2016 and in 2020 among Black, Latinx, and Asian American voters. We propose the same embrace of social hierarchies that motivated White support for Trump also motivated the political preferences and behaviors of a significant number of non-White Americans. Using several national large-N surveys conducted between 2011 and 2021 with large samples of Black, Latino, and Asian Americans, we explore how support for existing status hierarchies---both gender and racial---engendered support for Trump across racial and ethnic groups and discuss implications for the future of electoral politics in a rapidly diversifying U.S.

Supporting data for:Tulloch, Ayesha I.T. (2020) Towards more equitable and inclusive conservation and ecology conferences”, Perspective in Nature Ecology and Evolution.4 worksheets:1. Conference Initiatives: Results of review supporting Table 1 and Table S2 in main text of paper. Indicates which of 30 conference events for 10 international conference and ecology conferences implemented different initiatives.To evaluate how ecology and conservation conferences support these principles, the actions and policies of 10 international conferences held by nine academic societies for ecology and conservation were reviewed. Data were collated for the past three events that had been held by each conference targeting an international audience: the biannual International Congress for Conservation Biology (ICCB), International Marine Conservation Congress (IMCC), European Ecological Federation (EEF) Conference and the Society for Ecological Restoration (SER) World Conference on Ecological Restoration, the annual conferences of the Ecological Society of America (ESA), Ecological Society of Australia (ESAus), British Ecological Society (BES) and Association for Tropical Biology and Conservation (ATBC), the conference of the International Association for Ecology (INTECOL), held once every 5 years, and the IUCN World Conservation Congress (WCC) held once every 4 years. Data came from conferences between 2009 and 2020. Data were sourced from conference websites, conference programs and marketing material. Initiatives of interest were those targeted on improving equity and diversity in sex, gender identity and sexual orientation, and associated diversity types and lifestyle choices ̶ marital status, family or carer responsibilities, pregnancy and breastfeeding and physical appearance are categorised according to three broad groups:(a) Minimising discrimination, harassment and implicit bias(b) Minimising barriers to attendance(c) Maximising opportunities for participation & education.2. Conference Affordability: Data on conference registration fees and discounts for students and developing countries.3. Conference Attendance: Data on conference attendee diversity provided by individual conferences and societies on websites and marketing material.4. Conference_equity_forR_200505: Input data (csv file) for GLMM code in R, provided in S3. Code for Statistical Models.

Female journalists were most likely to face gender-based violence online, a survey released in 2020 found, with 73 percent of respondents worldwide stating that they were most likely to experience sexist abuse via emails or social media messages. Nearly 60 percent of female journalists also reported physical violence in the workplace, and 13 endured such abuse at home.

BackgroundDespite the well-established worldwide phenomenon of “the feminisation of medicine,” in Italy, Urology remains a male-dominated field.ObjectiveThe aims of our work are to assess data on medical students' choice of surgical specialty in Italy to investigate if a gender-biased trend exists and to find the key points that influence the decision-making process when choosing a specialty, with a focus on Urology.DesignData about access to residency programs in 2017–2020 were analysed through descriptive statistics. Investigations concerning the decision-making process were carried through distribution of an online anonymous survey to Italian medical students.ResultsUrology was among the specialties with the lowest proportion of female residents in Italy in the last 4 years: 37 (29.4%) in 2017, 27 (21.4%) in 2018, 40 (26.7%) in 2019, and 57 (25.2%) in 2020. The total number of participants of the survey was 1409, of which only 341 declared being keen to pursue a career path in surgery. Out of the 942 students not interested in surgery, 46.2% females and 22.5% males indicated a “sexist environment” as one of the reasons. Overall, the main reason for medical students not choosing Urology is the lack of interest in the specialty. Furthermore, there is a different perception of Urology as a sexist environment between female (23.4%) and male (3.2%, p 

In March 2020, approximately 84 percent of female respondents in Chile said they considered the country to be sexist, while a lesser share of the men surveyed (73 percent) thought the same. The majority of Chilean respondents thought men's engagement in women's rights can improve gender equality.

A hand-labeled training (50,000 tweets labeled twice) and evaluation set (10,000 tweets labeled twice) for hate speech on Slovenian Twitter. The data files contain tweet IDs, hate speech type, hate speech target, and annotator ID. For obtaining the full text of the dataset, please contact the first author.

Hate speech type:

1. Appropriate - has no target

2. Inappropriate (contains terms that are obscene, vulgar; but the text is not directed at any person specifically) - has no target

3. Offensive (including offensive generalization, contempt, dehumanization, indirect offensive remarks)

4. Violent (author threatens, indulges, desires, or calls for physical violence against a target; it also includes calling for, denying, or glorifying war crimes and crimes against humanity)

Hate speech target:

1. Racism (intolerance based on nationality, ethnicity, language, towards foreigners; and based on race, skin color)

2. Migrants (intolerance of refugees or migrants, offensive generalization, call for their exclusion, restriction of rights, non-acceptance, denial of assistance…)

3. Islamophobia (intolerance towards Muslims)

4. Antisemitism (intolerance of Jews; also includes conspiracy theories, Holocaust denial or glorification, offensive stereotypes…)

5. Religion (other than above)

6. Homophobia (intolerance based on sexual orientation and / or identity, calls for restrictions on the rights of LGBTQ persons

7. Sexism (offensive gender-based generalization, misogynistic insults, unjustified gender discrimination)

8. Ideology (intolerance based on political affiliation, political belief, ideology… e.g. “communists”, “leftists”, “home defenders”, “socialists”, “activists for…”)

9. Media (journalists and media, also includes allegations of unprofessional reporting, false news, bias)

10. Politics (intolerance towards individual politicians, authorities, system, political parties)

11. Individual (intolerance toward any other individual due to individual characteristics; like commentator, neighbor, acquaintance )

12. Other (intolerance towards members of other groups due to belonging to this group; write in the blank column on the right which group it is)

Training dataset

The training set is sampled from data collected between December 2017 and February 2020. The sampling was intentionally biased to contain as much hate speech as possible. A simple model was used to flag potential hate speech content and additionally, filtering by users and by tweet length (number of characters) was applied. 50,000 tweets were selected for annotation.

Evaluation dataset

The evaluation set is sampled from data collected between February 2020 and August 2020. Contrary to the training set, the evaluation set is an unbiased random sample. Since the evaluation set is from a later period compared to the training set, the possibility of data linkage is minimized. Furthermore, the estimates of model performance made on the evaluation set are realistic, or even pessimistic, since the evaluation set is characterized by a new topic: Covid-19. 10,000 tweets were selected for the evaluation set.

Annotation results

Each tweet was annotated twice: In 90% of the cases by two different annotators and in 10% of the cases by the same annotator. Special attention was devoted to evening out the overlap between annotators to get agreement estimates on equally sized sets.

Ten annotators were engaged for our annotation campaign. They were given annotation guidelines, a training session, and a test on a small set to evaluate their understanding of the task and their commitment before starting the annotation procedure. Annotator agreement in terms of Krippendorff Alpha is around 0.6. Annotation agreement scores are detailed in the accompanying report files for each dataset separately.

The annotation process lasted four months, and it required about 1,200 person-hours for the ten annotators to complete the task.

In 2020, female founders experienced significantly more sexist and sexual harassment in the workplace than male founders globally. For instance, there is a significant difference between the proportion of men and women who experienced harassment of a sexist or sexual nature with ************** of female founders reporting having experienced sexism in the workplace, while only ** percent of men reported having experienced this. Similarly, ** percent of women reported having experienced sexual harassment in the workplace, while this was the case for only ** percent of men. No male founders reported ever having experienced stalking, while ** percent of female founders in tech have experienced this.

Overall, a larger proportion of men experienced harassment in the form of physical assault, homophobia, physical abilities, and transphobia. For instance, homophobia was twice as prevalent for men when compared with their female counterparts. While only **** percent of women reported having experienced workplace harassment based on their religious affiliation, ** percent of men reportedly experienced this.

The prevalence of bias in the news media has become a critical issue, affecting public perception on a range of important topics such as political views, health, insurance, resource distributions, religion, race, age, gender, occupation, and climate change. The media has a moral responsibility to ensure accurate information dissemination and to increase awareness about important issues and the potential risks associated with them. This highlights the need for a solution that can help mitigate against the spread of false or misleading information and restore public trust in the media.Data description: This is a dataset for news media bias covering different dimensions of the biases: political, hate speech, political, toxicity, sexism, ageism, gender identity, gender discrimination, race/ethnicity, climate change, occupation, spirituality, which makes it a unique contribution. The dataset used for this project does not contain any personally identifiable information (PII).The data structure is tabulated as follows:Text: The main content.Dimension: Descriptive category of the text.Biased_Words: A compilation of words regarded as biased.Aspect: Specific sub-topic within the main content.Label: Indicates the presence (True) or absence (False) of bias. The label is ternary - highly biased, slightly biased and neutralToxicity: Indicates the presence (True) or absence (False) of bias.Identity_mention: Mention of any identity based on words match.Annotation SchemeThe labels and annotations in the dataset are generated through a system of Active Learning, cycling through:Manual LabelingSemi-Supervised LearningHuman VerificationThe scheme comprises:Bias Label: Specifies the degree of bias (e.g., no bias, mild, or strong).Words/Phrases Level Biases: Pinpoints specific biased terms or phrases.Subjective Bias (Aspect): Highlights biases pertinent to content dimensions.Due to the nuances of semantic match algorithms, certain labels such as 'identity' and 'aspect' may appear distinctively different.List of datasets used : We curated different news categories like Climate crisis news summaries , occupational, spiritual/faith/ general using RSS to capture different dimensions of the news media biases. The annotation is performed using active learning to label the sentence (either neural/ slightly biased/ highly biased) and to pick biased words from the news.We also utilize publicly available data from the following links. Our Attribution to others.MBIC (media bias): Spinde, Timo, Lada Rudnitckaia, Kanishka Sinha, Felix Hamborg, Bela Gipp, and Karsten Donnay. "MBIC--A Media Bias Annotation Dataset Including Annotator Characteristics." arXiv preprint arXiv:2105.11910 (2021). https://zenodo.org/records/4474336Hyperpartisan news: Kiesel, Johannes, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. "Semeval-2019 task 4: Hyperpartisan news detection." In Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 829-839. 2019. https://huggingface.co/datasets/hyperpartisan_news_detectionToxic comment classification: Adams, C.J., Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, Nithum, and Will Cukierski. 2017. "Toxic Comment Classification Challenge." Kaggle. https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge.Jigsaw Unintended Bias: Adams, C.J., Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum. 2019. "Jigsaw Unintended Bias in Toxicity Classification." Kaggle. https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification.Age Bias : Díaz, Mark, Isaac Johnson, Amanda Lazar, Anne Marie Piper, and Darren Gergle. "Addressing age-related bias in sentiment analysis." In Proceedings of the 2018 chi conference on human factors in computing systems, pp. 1-14. 2018. Age Bias Training and Testing Data - Age Bias and Sentiment Analysis Dataverse (harvard.edu)Multi-dimensional news Ukraine: Färber, Michael, Victoria Burkard, Adam Jatowt, and Sora Lim. "A multidimensional dataset based on crowdsourcing for analyzing and detecting news bias." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3007-3014. 2020. https://zenodo.org/records/3885351#.ZF0KoxHMLtVSocial biases: Sap, Maarten, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. "Social bias frames: Reasoning about social and power implications of language." arXiv preprint arXiv:1911.03891 (2019). https://maartensap.com/social-bias-frames/Goal of this dataset :We want to offer open and free access to dataset, ensuring a wide reach to researchers and AI practitioners across the world. The dataset should be user-friendly to use and uploading and accessing data should be straightforward, to facilitate usage.If you use this dataset, please cite us.Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by Shaina Raza, Vector Institute is licensed under CC BY-NC 4.0

Most young Italians would engage in the fight against discrimination and violence for gender and sexual orientation. According to a recent survey, over 90 percent of respondents aged between 13 and 23 years would undertake activities for this cause. More than half of them would do it for one or more hours a week. On the other hand, about six percent of interviewees would not personally take part in this fight.

In 2020, more women who were founders in the technology industry experienced at least one instance of gender discrimination in the workplace than men, on average. More than half of female founders in the tech industry felt they have experienced differential treatment while raising funding because of their gender, while only ** percent of male founders also reported feeling this way. Additionally, **** percent more women than men reported being told at least once that they would be more likely to get funded if they were a man or had a male cofounder. However, ** percent of male founders reported having experienced an investor stealing their idea, while only ** percent of female founders reported having experienced this.

The Gender Equality Index benchmarks national gender gaps on economic, political, education, and health-based criteria among the countries of the European Union. A score of 0 indicates that there is no gender equality, while 100 points indicate that gender equality is achieved. In the 2024 index, the leading country was Sweden with 82 points. Denmark and the Netherlands were the second and third most gender equal countries. Considering the other side of the spectrum, Romania only scored 56.1 points, way below the EU average of 70.2. Other countries at the bottom of the ranking were Hungary and Romania. Equality in health Not only does the index measure gender equality on national levels, it also breaks down gender equality into different dimensions. With an index score of 88 points, health was the most equal dimension among men and women within the EU, followed by money and work. To the contrary, power was considered the most unequal dimension, along with knowledge and time management. The Global Gender Gap Index From a global perspective, Iceland is considered the most gender equal country. Dominating this list are the Nordic countries: Norway, Finland, New Zealand, and Sweden rank in the top 5. As of 2024, it was estimated that Europe had closed 75 percent of its gender gap, making it the most successful region in the world, before North and Latin America. Nevertheless, experts predict that gender parity will not be achieved in the region for another 67 years.