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51 results for “Misinformation”
CMU-MisCov19: A Novel Twitter Dataset for Characterizing COVID-19 Misinformation
<p>From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. Detection and characterization of misinformation requires an availability of annotated datasets. Most of the published COVID-19 Twitter datasets are generic, lack annotations or labels, employ automated annotations using transfer learning or semi-supervised methods, or are not specifically designed for misinformation. Annotated datasets are either only focused on "fake news", are small in size, or have less diversity in terms of classes.</p> <p>Here, we present a novel Twitter misinformation dataset called <strong>"CMU-MisCov19"</strong> with 4573 annotated tweets over 17 themes around the COVID-19 discourse. We also present our annotation codebook for the different COVID-19 themes on Twitter, along with their descriptions and examples, for the community to use for collecting further annotations. Further details related to the dataset, and our analysis based on this dataset can be found at <a href="https://arxiv.org/abs/2008.00791">https://arxiv.org/abs/2008.00791</a>. In adherence to the Twitter’s terms and conditions, we do not provide the full tweet JSONs but provide a ".csv" file with the tweet IDs so that the tweets can be rehydrated. We also provide the annotations, and the date of creation for each tweet for the reproduction of the results of our analyses.</p> <p><strong>Note: If for any reason, you are not able to rehydrate all the tweets, reach out to Shahan Ali Memon at (shahan@nyu.edu).</strong></p> <p>If you use this data, please cite our paper as follows: </p> <p><em>"Shahan Ali Memon and Kathleen M. Carley. Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset, In Proceedings of The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020), co-located with CIKM, virtual event due to COVID-19, 2020."</em></p>
A Novel Dataset of Misinformation Tweets Regarding the CoronaVac Vaccine in Brazil
<p>This dataset was built to analyze the spread of misinformation about CoronaVac in Brazil by using data from Twitter for two specific events: the approval for emergency use in adults over 18 years old (January 17, 2021) and the approval for use in children aged 6 to 17 years (January 20, 2022).</p> <p>We choose to label the original tweets with at least one retweet in the analyzed period. The manual labeling of such tweets was initially performed by two annotators with high knowledge about the dataset and the considered context. In cases in which there was no agreement between the two annotators, a third annotator was considered to define the class of the tweet. </p> <p>The final dataset contains <strong>1,010 tweets from January 17, 2021</strong>, and <strong>816 tweets from January 20, 2022</strong>.</p> <p>This dataset was originally built for a conference paper accepted at BraSNAM 2022. If you make use of the dataset, please also cite the following paper:</p> <p><em>Gabriel P. Oliveira, Beatriz F. Paiva, Ana Paula Couto da Silva, and Mirella M. Moro. Characterizing the Diffusion of Misinformation Regarding the CoronaVac Vaccine in Brazil. In Proceedings of the XI Brazilian Workshop on Social Network Analysis and Mining </em><em>(BraSNAM 2022), 2022.</em></p> <pre><code>@inproceedings{brasnam/OliveiraPSM22, title = {Characterizing the Diffusion of Misinformation Regarding the CoronaVac Vaccine in Brazil}, author = {Gabriel P. Oliveira and Beatriz F. Paiva and Ana Paula Couto da Silva and Mirella M. Moro}, booktitle = {Proceedings of the XI Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)} year = {2022} }</code></pre>
MHMisinfo - Video-based Mental Health Misinformation Dataset
<p>MHMisinfo-Gold and MHMisinfo-Large datasets, as described in the paper "Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms" (forthcoming at ICWSM 2025). Videos and comments for each dataset are seperately stored in different .csv files</p> <p><strong>Dataset schema, videos</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>video_id</strong></td> <td>ID of the Video, as assigned by their respective platforms</td> </tr> <tr> <td><strong>video_title</strong></td> <td>The title of the video</td> </tr> <tr> <td><strong>video_description</strong></td> <td>The description of the video, given by the video creators</td> </tr> <tr> <td><strong>audio_transcript</strong></td> <td>Text transcription of the video's audio track, as generated by Whisper speech-to-text model</td> </tr> <tr> <td><strong>video_view_count</strong></td> <td>View count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_like_count</strong></td> <td>Like count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_comment_count</strong></td> <td>Comment count of the video, at the time of data collection</td> </tr> <tr> <td><strong>label_ioi</strong></td> <td>"Information of Interventions" label of video, annotated by experts. 1 = High-quality information on interventions, -1 = Low-quality information on interventions</td> </tr> <tr> <td><strong>label_ebt</strong></td> <td>"Evidence-based Treatment" label of video, annotated by experts. 1 = Encourages evidence-based treatment, -1 = Discourages evidence-based treatment</td> </tr> <tr> <td><strong>label_aoc</strong></td> <td>"Alignment of Consensus" label of video, annotated by experts, 1 = High Alignment with Consensus, -1 = Low Alignment with Consensus</td> </tr> <tr> <td><strong>label</strong></td> <td>Overall mental health misinformation label of the video. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> <tr> <td><strong>platform</strong></td> <td>Platform of the video</td> </tr> </tbody> </table> <p><strong>Dataset schema, comments</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>text</strong></td> <td>The raw text of the comment</td> </tr> <tr> <td> <p><strong>commenter_channel_display_name</strong></p> </td> <td>The display name of the user who posted the comment.</td> </tr> <tr> <td> <p><strong>comment_publish_date</strong></p> </td> <td>The time when the comment was orignally published, .</td> </tr> <tr> <td> <p><strong>video_id</strong></p> </td> <td>ID of the Video associated by the platform, as assigned by their respective platforms</td> </tr> <tr> <td> <p><strong>platform</strong></p> </td> <td>Platform of the video associated with the comment</td> </tr> <tr> <td> <p><strong>label</strong></p> </td> <td>Overall mental health misinformation label of the video associated with the comment. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> </tbody> </table> <p> </p>
A Hierarchical Network-Oriented Analysis of UserParticipation in Misinformation Spread on WhatsApp
<p>#Authors: Gabriel Peres Nobre, Carlos Henrique Gomes Ferreira, Jussara Marques de Almeida<br> #2021</p> <p>Script to read a Database file of messages and, in the end, extract user communities based on content co-sharing.</p> <p>We provide a database file with the anonymized messages shared in WhatsApp. </p>
Combining Policies to Reduce the Spread of Viral Misinformation Online
<p>Data were collected as part of the Election Integrity Partnership. Instances of potential misinformation were flagged as tickets. These were reviewed and categorized as misinformation if they made false claims related to election integrity. Details on the collection methods of the EIP can be found in our Report. These tickets were grouped together into qualitatively similar incidents. For example, tickets regarding false narratives about the use of Benford's law to detect fraud in Wisconsin became an incident. For each incident, search terms and appropriate data-ranges were determined to query our database. </p> <p>Our full database consisted of all tweets matching an evolving set of keywords, collected in real time, using the Twitter API. To maintain user privacy, we are providing data segmented into events and aggregated into 5-minute blocks of time. This should be sufficient for replicating our findings (predicated on the aggregation and segmentation). In order to permit analysis under various user-removal conditions, we have provided multiple versions of this dataset with users removed according to the conditions evaluated in the manuscript. We encourage anyone with the need for more granular data or alternate conditions to reach out to the University of Washington Center for an Informed Public.</p>
Gajderowicz, B., Fisher, A., Mago, V.: (preperation) "Graph pruning for identifying COVID-19 misinformation dissemination patterns and indicators on Twitter/X"
<p>This dataset is for the repository <a href="https://github.com/bgajdero/social-graph-analysis-2024">https://github.com/bgajdero/social-graph-analysis-2024</a>.</p>
To Inform or Misinform: The Current Situation of Brazil's Information Ecosystem
<p>Originally published on Youtube on March 12th, 2024: https://www.youtube.com/watch?v=h4LJxZjt6Mg</p> <p>Digitalisation has transformed the way information is created and disseminated. Currently, we are grappling with the darker side of this transformation, wherein vicious, mutually reinforcing cycles of disinformation, extreme polarization, and autocratization threaten peace, justice, and good governance. Brazil is no exception. Factors that shape information ecosystems are highly dependent on context, yet debates on how to counter the "information disorder" we are currently experiencing tend to be highly specific, preponderantly focusing on the regulation of online content. This webinar invites participants to look at the bigger picture. Bringing together disinformation experts focused on the media, politics, law, and socioeconomic roots and impacts of disinformation, the goal of this workshop is to identify risks and opportunities in Brazilian society. The larger question is how do we restore order to our information ecosystems?</p>
COVID19.BR: A Dataset of Misinformation about COVID-19 in Brazilian Portuguese WhatsApp Messages
<p>COVID19.BR is provided in a csv file where the columns are date, hour, phone number, international phone code, if the user is Brazilian its state, the text content of the message, word count, character count, and if the message contained media (audio, image, or video). Each row represents a WhatsApp message.</p>
Covid_19 Misinformation Detection Dataset
<p>This data contains tweets collected from fact-checking websites and reliable organizations with expertise relating to covid_19. It is useful for spotting misinformation among facts.</p> <div class="chakra-portal"> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> </div>
COVID-19 Misinformation Dataset
<p>The dataset and the annotation codebook from "Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the COVID-19 Infodemic" (accepted at RANLP 2023)</p>
Capturing Pertinent Symbolic Features for Enhanced Content Based Misinformation Detection
<p>Datasets for the paper <em>Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection</em>. The compressed folder contains <strong><em>annotated</em></strong> data carried out using the symbolic models presented in the paper.</p>
Misinformation of COVID-19 on Twitter
<p>The dataset of Misinformation of COVID-19 on Twitter is crawled from the social media Twitter. This data set contains Twitter crawling about the tweets of the Indonesian COVID-19 community. The data is used as preliminary data for classifying misinformation tweets about COVID-19.The dataset is in xlsx file.</p>
Twitter vaccine misinformation data
<p>Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this disparity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, pro-vaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics.</p>
Datasets for the paper: Lost in Translation: Using Global Fact-Checks to Measure Multilingual Misinformation Prevalence, Spread, and Evolution
<p>FullData.csv.gz: Contains links to all claims in the data-set.</p> <ul> <li>publishing_date: Date on which the fact-check was published.</li> <li>claim_date: Date that claim was made.</li> <li>verdict: Rating given by the fact-checking organisation.</li> <li>language: Language of the claim.</li> <li>cluster_{threshold}: ID of the cluster that claim belongs to at all given clusters. Entry "0" means that claim is singleton and not clustered with any other claims.</li> </ul> <p>Embeddings.npy: Contains a dictionary linking each claim to it's embedding calculated with LaBSE.</p>
Anatomy of an online misinformation network
<p>This dataset is provided to facilitate reproducibility of results presented in the following paper:</p> <p><strong>Chengcheng Shao, Pik-Mai Hui, Lei Wang, Xinwen Jiang, Alessandro Flammini, Filippo Menczer and Giovanni Luca Ciampaglia (2018): Anatomy of an online misinformation network. Preprint arXiv:1801.06122, arxiv.org/abs/1801.06122 </strong></p> <p>Please read carefully both the paper and the README file attached to understand what is contained in this dataset before proceeding. These data are provided for non-commercial purposes only. If you use this dataset for research, please be sure to cite the above preprint, or preferably the final published version that will be shown on the arXiv.</p>
Domain shares on Twitter containing news and misinformation
<p>This dataset contains a set of domain sharing actions that occurred on Twitter during the month of June 2017. Each domain sharing action can be thought of as a triple <em>(user_id, action_id, domain).</em> The user_id is an anonymized Twitter account ID, and the action_id is an anonymized tweet ID. The tweets from which the dataset was created were collected through Twitter's decahose API. Each user in the dataset was responsible for sharing at least one news article, and at least one article that can be labeled as misinformation. </p> <p>The data is distributed in <em>domain-shares.data</em> in the following JSON format:</p> <pre><code class="language-json">{ "<user-id>": { "<action-id>": ["<domain>", "<domain>", ...], ... }, ... }</code></pre> <p>For example:</p> <pre><code class="language-json">{ "22359b28-93e1-4c13-a3eb-e72357b77c65": { "1": ["palmerreport.com"], "2": ["reuters.com", "abcn.ws"], "3": ["mobile.nytimes.com"] }, "ffe79a32-d49d-4780-87b7-bb6417106067": { "4": ["dallasnews.com"] } }</code></pre> <p>In addition, a TAB-separated version with <em>(user id, action id, domain)</em> triples is also available.</p> <p>For further information on how the dataset was constructed and on analyses that have been conducted on it, please refer to the accompanying Github repository at <a href="https://github.com/dimitargnikolov/twitter-bias">https://github.com/dimitargnikolov/twitter-bias</a>.</p>
Misinformation in the classroom: perceptions of Journalism students
<p>This data results are used in an article that studied the perception of undergraduate Journalism students at a University in the mountains of Santa Catarina (Brazil) about misinformation.</p>
Misinformation, internet honey trading, and beekeepers drive a plant invasion
<p>Biological invasions are a major human induced global change that is threatening global biodiversity by homogenizing the world's fauna and flora. Species spread because humans have moved species across geographic boundaries and have changed ecological factors that structure ecosystems, such as nitrogen deposition, disturbance, etc. Many biological invasions are caused accidentally, as a byproduct of human travel and commerce driven product shipping. However, humans also have spread many species intentionally because of perceived benefits. Of interest is the role of the recent exponential growth in information exchange via internet social media in driving biological invasions. To date, this has not been examined. Here we show that for one such invasive species, goldenrod, social networks spread misleading and incomplete information that is enhancing the spread of goldenrod invasions into new environments. We show that the notion of goldenrod honey as a "superfood" with unsupported healing properties is driving a demand that leads beekeepers to produce goldenrod honey. Social networks provide a forum for such information exchange and this is leading to further spread of goldenrod in many countries where goldenrod is not native, such as Poland. However, this informal social information exchange ignores laws that focus on preventing the further spread of invasive species and the strong negative effects that goldenrod has on native ecosystems, including floral resources that negatively impact honeybee performance. Thus, scientifically unsupported information on "superfoods" such as goldenrod honey that is disseminated through social internet networks has real world consequences such as increased goldenrod invasions into novel geographical regions which decreases native biodiversity.</p>
#Coronavirus on TikTok: User engagement with misinformation as a potential threat to public health behavior
<p><strong>Background:</strong> COVID-related misinformation is prevalent online, including on social media. The purpose of this study was to explore factors associated with user engagement with COVID-related misinformation on the social media platform, TikTok.</p> <p><strong>Methods:</strong> A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior.</p> <p><strong>Results:</strong> 166 TikTok videos were identified. Moderate misinformation was present in 36 (22%) videos, and high-level misinformation was present in 11 (7%). After controlling for characteristics and content, videos containing moderate misinformation were less likely to generate a user response indicating intended behavior change. By contrast, videos containing high-level misinformation were less likely to be viewed but demonstrated a non-significant trend towards higher engagement among viewers.</p> <p><strong>Conclusions:</strong> COVID-related misinformation is less frequently viewed on TikTok but more likely to engage viewers. Public health authorities can combat misinformation on social media by posting content of their own. </p>
Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter
<p>This is the released dataset of our paper <em>Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter</em>.</p> <p>Due to Twitter policy, we can only share the IDs of tweets that are used in our research. </p> <p>Please feel free to contact yupingw@bu.edu if you have any further questions. </p>
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