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6 results for “Biased news”
Navigating News Narratives: A Media Bias Analysis Dataset
<p>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.</p><p><strong>Data description: </strong>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).</p><p><strong>Data Format: </strong>The format of data is:</p><ul><li>ID: Numeric unique identifier.</li><li>Text: Main content.</li><li>Dimension: Categorical descriptor of the text.</li><li>Biased_Words: List of words considered biased.</li><li>Aspect: Specific topic within the text.</li><li>Label: Neutral, Slightly Biased , Highly Biased</li></ul><p><br><strong>Annotation Scheme: </strong>The annotation scheme is based on Active learning, which is Manual Labeling --> Semi-Supervised Learning --> Human Verifications (iterative process)</p><ul><li>Bias Label: Indicate the presence/absence of bias (e.g., no bias, mild, strong).</li><li>Words/Phrases Level Biases: Identify specific biased words/phrases.</li><li>Subjective Bias (Aspect): Capture biases related to content aspects.</li></ul><p><br><strong>List of datasets used : </strong>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.</p><p>We also utilize publicly available data from the following links. Our Attribution to others.</p><p> <strong>MBIC (media bias): </strong>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). <a href="https://zenodo.org/records/4474336">https://zenodo.org/records/4474336</a> </p><p><strong>Hyperpartisan news: </strong>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. <a href="https://huggingface.co/datasets/hyperpartisan_news_detection">https://huggingface.co/datasets/hyperpartisan_news_detection</a> </p><p><strong>Toxic comment classification: </strong>Adams, C.J., Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, Nithum, and Will Cukierski. 2017. "Toxic Comment Classification Challenge." Kaggle. <a href="https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge">https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge</a>.</p><p><strong>Jigsaw Unintended Bias: </strong>Adams, C.J., Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum. 2019. "Jigsaw Unintended Bias in Toxicity Classification." Kaggle. <a href="https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification">https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification</a>.</p><p><strong>Age Bias : </strong>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. <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/F6EMTS">Age Bias Training and Testing Data - Age Bias and Sentiment Analysis Dataverse (harvard.edu)</a></p><p><strong>Multi-dimensional news Ukraine: </strong>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. <a href="https://zenodo.org/records/3885351#.ZF0KoxHMLtV">https://zenodo.org/records/3885351#.ZF0KoxHMLtV</a> </p><p><strong>Social biases: </strong>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). <a href="https://maartensap.com/social-bias-frames/">https://maartensap.com/social-bias-frames/</a> </p><p> </p><p><strong>Goal of this dataset :</strong>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.</p><p><strong>If you use this dataset, please cite us.</strong></p><p>Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by <a href="https://www.linkedin.com/in/shainaraza/">Shaina Raza, Vector Institute </a>is licensed under <a href="http://creativecommons.org/licenses/by-nc/4.0/?ref=chooser-v1">CC BY-NC 4.0 </a></p><p> </p>
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
<p>Data supporting "Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News". <br><br></p> <p> If you use this dataset in your own research, please cite this paper:</p> <p>```<br>@misc{abels2024mitigating,<br> title={Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News}, <br> author={Axel Abels and Elias Fernandez Domingos and Ann Nowé and Tom Lenaerts},<br> year={2024},<br> eprint={2403.08829},<br> archivePrefix={arXiv},<br> primaryClass={cs.HC}<br>}<br>```</p> <p> </p> <table> <tbody> <tr> <td><strong>column name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>treatment</td> <td>identifier for the set of headlines presented to the participant</td> </tr> <tr> <td>trial</td> <td>trial/round in which the headline was presented </td> </tr> <tr> <td>arm</td> <td>which "arm" the headline was presented as (0=left, 1=middle, 2=right)</td> </tr> <tr> <td>advice</td> <td>the participant's response (0=very unlikely, 0.25=unlikely, 0.5=undecided, 0.75=likely, 1=very likely)</td> </tr> <tr> <td>genuine</td> <td>whether the headline was genuine (1) or altered (0)</td> </tr> <tr> <td>headline</td> <td>the headline as shown to the participant</td> </tr> <tr> <td>original</td> <td>the headline before a possible alteration</td> </tr> <tr> <td>expert_id</td> <td>participant's identifier</td> </tr> <tr> <td>sentiment</td> <td>whether the headline reported a negative (-1) or positive (1) outcome</td> </tr> <tr> <td>expert:ethnicity</td> <td>the participant's ethnicity</td> </tr> <tr> <td>expert:sex</td> <td>the participant's sex</td> </tr> <tr> <td>expert:age</td> <td>the participant's age</td> </tr> <tr> <td>outcome:white, outcome:black, outcome:young, outcome:old, outcome:male, outcome:female</td> <td>whether the headline reported a negative (-1) or positive (1) or neutral (0) outcome for the specified group</td> </tr> <tr> <td>trial_time</td> <td>how long the participant took to respond to the trial/round</td> </tr> </tbody> </table> <p><strong>abstract</strong><br>Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influence these biases can have in the pervasive problem of fake news by evaluating human participants' capacity to identify false headlines. By focusing on headlines involving sensitive characteristics, we gather a comprehensive dataset to explore how human responses are shaped by their biases. Our analysis reveals recurring individual biases and their permeation into collective decisions. We show that demographic factors, headline categories, and the manner in which information is presented significantly influence errors in human judgment. We then use our collected data as a benchmark problem on which we evaluate the efficacy of adaptive aggregation algorithms. In addition to their improved accuracy, our results highlight the interactions between the emergence of collective intelligence and the mitigation of participant biases. </p>
Webis-News-Bias-20
<p>This is the dataset used in the paper "<a href="https://webis.de/publications.html#chen_2020b">Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity.</a>"</p> <p>The news articles are stored in json format, one news each line. For each news, there are following fields:</p> <p> - title: the title of the news<br> - content: the content of the news<br> - source: the news portal<br> - allsides_bias: the bias indicated in the allsides.com (left, center, or right)<br> - misc: other information, such as author, date, and topics<br> - adfontes_fair: the labels from adfontesmedia.com, whehter the article is fair or not (bias, neutral, or unknown)<br> - adfontes_political: the labels from adfontesmedia.com, whether the article is political bias or not (bias, neutral, or unknown)<br> - event_id: the event id. Articles with the same event have the same id</p> <p> </p>
Results and further resources concerning our pre-studies concerning revealing biases in news articles
<p>Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may strongly influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.</p>
A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing
<p>We provide a large data set consisting of <strong>2,057 sentences</strong> from 90 news articles and annotations of crowdworkers with respect to <strong>bias itself</strong> and the following <strong>bias dimensions</strong>:</p> <ol> <li><strong>hidden assumptions</strong></li> <li><strong>subjectivity</strong></li> <li><strong>representation tendencies</strong></li> </ol> <p>Our data set contains <strong>44,547 labels in total</strong> (43,197 sentence labels and 1,350 article labels).</p> <p>The news articles deal with the <strong>Ukraine crisis</strong>. They were published in 33 countries in total and were selected based on the data set of Cremisini et al. (Cremisini, A., Aguilar, D., & Finlayson, M. A. <em>A Challenging Dataset for Bias Detection: The Case of the Crisis in the Ukraine</em>, Proc. of SBP-BRiMS'19, pp. 173-183, 2019).</p> <p>Each sentence was annotated by 5 crowdworkers. In total, we spent $ 3,335 for the crowdworkers annotations.</p> <p>More information can be found in our <a href="https://github.com/michaelfaerber/ukraine-news-bias">GitHub repository</a>. A description of the used file format is given in the codebook attached to the dataset.</p> <p>Please cite our data set as follows:</p> <pre><code>@unpublished{Faerber2020Bias, author = {Michael F{\"{a}}rber and Victoria Burkard and Adam Jatowt and Sora Lim}, title = {{A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing}}, year = {2020} }</code></pre>
Rewritten Media Bias News Headlines
<p>This dataset was used in the paper "Rewriting Bias: Mitigating Media Bias in News Recommender Systems through Automated Rewriting" by Qin Ruan, Jin Xu, Susan Leavy, Brian Mac Namee, and Ruihai Dong, presented at the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP'24), July 1-4, 2024, in Cagliari, Italy. (ACM, New York, NY, USA, 10.1145/3627043.3659541).</p> <p>The dataset includes seven distinct rewritten versions of news headlines generated using the following methods proposed in the paper: RADJ, RADV, RNOUN, RVERN, RALL, RG3.5, and RG4.0.</p> <p>The rewriting approaches are categorised into two main categories: <strong>Word Replacement </strong>approaches and <strong>Large Language Models </strong>approaches.</p> <p>1.Sentence Rewriting Using Word Replacement :</p> <ul> <li><strong>Replace Adjectives (RADJ)</strong>: This approach replaces adjectives identified as contributing to bias with neutral or opposite ones.</li> <li><strong>Replace Adverbs (RADV)</strong>: This approach replaces adverbs identified as contributing to bias with neutral or opposite ones.</li> <li><strong>Replace Nouns (RNOUN)</strong>: This approach replaces nouns identified as contributing to bias with neutral or more general ones.</li> <li><strong>Replace Verbs (RVERB)</strong>: This approach replaces verbs identified as contributing to bias with neutral or more factual ones.</li> <li><strong>Replace All (RALL)</strong>: This is a combination of all the above approaches.</li> </ul> <p>2.Sentence Rewriting Using Large language Models:</p> <ul> <li><strong>Sentence Rewriting using GPT-3.5 (RG3.5)</strong>: This method rephrases sentences using GPT-3.5 based on a specific prompt.</li> <li><strong>Sentence Rewriting using GPT-4.0 (RG4.0)</strong>: This method rephrases sentences using GPT-4.0 based on a specific prompt.</li> </ul> <p>The dataset consists of original news headlines and their corrsponding rewritten versions prodcued by each of the seven approaches mentioned above. Each rewritten version aims to reduce bias while maintaining the original meaning.</p> <p> </p> <p><strong>Dataset Structure:</strong></p> <p>The dataset consists of a single file with the following columns:</p> <ul> <li>NewsId: A unique identifier for each news article.</li> <li>Original: The original headline of the news article.</li> <li>RG3.5: The headline rewritten using the <a href="https://platform.openai.com/docs/models/gpt-3-5">GPT-3.5</a> model.</li> <li>RG4.0: The headline rewritten using the <a href="https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo">GPT-4.0</a> model.</li> <li>RADJ: The headline with adjectives replaced to reduce bias.</li> <li>RADV: The headline with adverbs replaced to reduce bias.</li> <li>RVERB: The headline with verbs replaced to reduce bias.</li> <li>RNOUN: The headline with nouns replaced to reduce bias.</li> <li>RALL: The headline with adjectives, adverbs, verbs. noun replaced to reduce bias.</li> </ul> <p><strong>Usage:</strong></p> <p>This dataset can be used to study the impact of different sentence rewriting approaches on reudicng bias in enws healines. It is also suitable for researchers interested in natural language processing, media bias reduction, and the application of large language models in generative AI.</p> <p>For more information, please visit <a href="https://github.com/ruanqin0706/MediaBiasinNewsRec">https://github.com/ruanqin0706/MediaBiasinNewsRec</a></p> <p><strong>Contact Information: </strong>qin.ruan@ucdconnect.ie</p>
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