Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “political bias”
Dataset for "Is Wikipedia Politically Biased?"
<p><span><span>·<span> </span></span></span><span>This work aims to determine whether there is evidence of political bias in English Wikipedia articles.</span></p> <p><span><span>·<span> </span></span></span><span>Wikipedia is one of the most visited domains on the Web, attracting hundreds of millions of unique users per month. Wikipedia content is also routinely used for training Large Language Models (LLMs), which are the core engines driving cutting edge AI systems.</span></p> <p><span><span>·<span> </span></span></span><span>To study political bias in Wikipedia content, we analyze the sentiment (<em>positive</em>, <em>neutral</em> or <em>negative</em>) with which a set of target terms (N=1,628) with political connotations (i.e. names of recent U.S. presidents, U.S. congressmembers, U.S. Supreme Court Justices, or Prime Ministers of Western countries) are used in Wikipedia articles. </span></p> <p><span><span>·<span> </span></span></span><span>We do not cherry pick the set of terms to be included in the analysis but instead use publicly available pre-existing lists of terms from Wikipedia and other sources.</span></p> <p><span><span>·<span> </span></span></span><span>We find a mild to moderate tendency in Wikipedia articles to associate public figures politically aligned right-of-center with more negative sentiment than left-of-center public figures.</span></p> <p><span><span>·<span> </span></span></span><span>These favorable associations for left-leaning public figures are apparent for names of recent U.S. Presidents, U.S. Supreme Court Justices, U.S. Senators, U.S. House of Representatives Congressmembers, U.S. State Governors, Western countries’ Prime Ministers, and prominent U.S. based journalists and media organizations.</span></p> <p><span><span>·<span> </span></span></span><span>Despite being common, these politically asymmetrical sentiment associations are not ubiquitous. We find no evidence of them in the sentiment with which names of U.K. MPs and U.S. based think tanks are used in Wikipedia articles.</span></p> <p><span><span>·<span> </span></span></span><span>We also find larger associations of negative emotions (i.e. <em>anger</em> and <em>disgust</em>) with right-leaning public figures and positive emotion (i.e. <em>joy</em>) with left-leaning public figures.</span></p> <p><span><span>·<span> </span></span></span><span>The trends just described constitute suggestive evidence of political bias embedded in Wikipedia articles.</span></p> <p><span><span>·<span> </span></span></span><span>We also find some of the aforementioned sentiment political associations embedded in Wikipedia articles popping up in OpenAI’s language models. This is suggestive of the potential for biases in Wikipedia content percolating into widely used AI systems.</span></p> <p><span><span>·<span> </span></span></span><span>Wikipedia’s <em>neutral point of view policy</em> (NPOV) aims for articles in Wikipedia to be written in an impartial and unbiased tone. Our results suggest that Wikipedia’s <em>neutral point of view policy</em> is not achieving its stated goal of political viewpoint neutrality.</span></p> <p><span><span>·<span> </span></span></span><span>This report highlights areas where Wikipedia can improve in how it presents political information. Nonetheless, we want to acknowledge Wikipedia’s significant and valuable role as a public resource. We hope this work inspires efforts to uphold and strengthen Wikipedia’s principles of neutrality and impartiality.</span></p> <p>The set of 1,653 target terms used in our analysis, the sample of Wikipedia paragraphs where they occur (as of 2022) and their sentiment and emotion annotations are provided in the files:</p> <p>- WikipediaParagraphsWithTargetNGramsAndSentiment.csv</p> <p>- WikipediaParagraphsWithTargetNGramsAndEmotion.csv</p>
Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification
<p>Code and dataset for paper "Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification". ICWSM 2025</p> <p>Preprint: https://arxiv.org/abs/2407.17688</p> <p>Citation: </p> <p>@misc{ng2024examininginfluencepoliticalbias,<br> title={Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification}, <br> author={Lynnette Hui Xian Ng and Iain Cruickshank and Roy Ka-Wei Lee},<br> year={2024},<br> eprint={2407.17688},<br> archivePrefix={arXiv},<br> primaryClass={cs.CL},<br> url={https://arxiv.org/abs/2407.17688}, <br>}</p>
Is GPT-4 Less Politically Biased than GPT-3.5? A Renewed Investigation of ChatGPT's Political Biases
Open the record for dataset details and reuse information.
Sentiment analysis of media's political bias in micro-blogging - A computational framework for optimized recommendation systems
<p>This dataset contains tweets from four Pakistani news channels, namely DAWN, GEO, ARY, and 24News, collected using the twarc command line tool with a Twitter academic researcher account. The tweets were collected between Nov, 2015, and April 4, 2022, and relate to three major political parties in Pakistan, PTI, PMLN, and PPP through their official names in the relevant news channel page tweets. </p>
Dataset to Analyze Propaganda in Tweets From Politically Biased Sources
<p><span>Our dataset consists of 32,047 tweets from 44 ac</span><span>count associated with 11 news outlets downloaded between</span><br><span>27th</span><span> </span><span>September 2022 to 3rd</span><span> </span><span>October 2022, from Twitter using </span><span>Twitter Streaming API.</span><br><span>Each tweet is a record that contains the tweet id, number </span><span>of likes, retweet count, isretweeted status, associated bias of</span><br><span>the new outlets according to AllSides Media Bias rating</span><span>, </span><span>and model prediction(1 for propaganda and 0 for non-propaganda)</span></p>
Perception biases of gender in political talk shows? A Twitter analysis of user engagement in Germany
<p><strong>Perception biases of gender in political talk shows?</strong></p> <p>This dataset contains the raw and processed data analysed in the CSS lecture at TUM. </p> <p> </p> <p><strong>Explanation</strong></p> <ul> <li>raw.zip: Contains the raw data structured by year it was created</li> <li>annotated_data.json: data as it was used for the analysis after filtering and combining with participant data</li> <li>participant_encoding.csv: encoded information about participants of talk shows</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.