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434 results for “Social media”
Dataset for "Geospatial analysis of toponyms in geotagged social media posts"
<h1>Geotagged Twitter posts dataset</h1> <p>Dataset used for the research presented in the following paper: Takayuki Hiraoka, Takashi Kirimura, Naoya Fujiwara (2024) "Geospatial analysis of toponyms in geo-tagged social media posts".</p> <p>We collected georeferenced Twitter posts tagged to coordinates inside the bounding box of Japan between 2012-2018. The present dataset represents the spatial distributions of all geotagged posts as well as posts containing in the text each of 24 domestic toponyms, 12 common nouns, and 6 foreign toponyms. The code used to analyze the data is available on <a href="https://github.com/takayukihir/geotagged-tweets" target="_blank" rel="noopener">GitHub</a>.</p> <h2>Data description</h2> <ul> <li><code>preprocessed_mcntlt7_selected/</code>: Number of geotagged twitter posts in each grid cell. Each csv file under this directory associates each grid cell (spanning 30 seconds of latitude and 45 secoonds of longitude, which is approximately a 1km x 1km square, specified by an 8 digit code <code>m3code</code>) with the number of geotagged tweets tagged to the coordinates inside that cell (<code>tweetcount</code>). <code>file_names.json</code> relates each of the toponyms studied in this work to the corresponding datafile (<code>all</code> denotes the full data). Note that these data files are modified from the v1.0.0 to exclude posts that contain seven or more mentions.</li> <li><code>population/population_center_2020.xlsx</code>: Center of population of each municipality based on the 2020 census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.stat.go.jp/data/kokusei/topics/topi135.html" target="_blank" rel="noopener">their website</a> (Japanese)</li> <li><code>population/census2015mesh3_totalpop_setai_area.csv</code>: Resident population in each grid cell based on the 2015 census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=1&toukeiCode=00200521&toukeiYear=2015&aggregateUnit=S&serveyId=S002005112015&statsId=T000846" target="_blank" rel="noopener">e-stat</a> (Japanese)</li> <li><code>population/economiccensus2016mesh3_jigyosyo_jugyosya_area.csv</code>: Employed population in each grid cell based on the 2016 Economic Census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=1&toukeiCode=00200553&toukeiYear=2016&aggregateUnit=S&serveyId=S002005112016&statsId=T000917" target="_blank" rel="noopener">e-stat</a> (Japanese)</li> <li><code>japan_MetropolitanEmploymentArea2015map/</code>: Shape file for the boundaries of Metropolitan Employment Areas (MEA) in Japan. See <a href="https://www.csis.u-tokyo.ac.jp/UEA/index_e.htm" target="_blank" rel="noopener">this website</a> for details of MEA.</li> <li><code>ward_shapefiles/</code>: Shape files for the boundaries of wards in large cities, published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=2&aggregateUnitForBoundary=A&toukeiCode=00200553&toukeiYear=2016&serveyId=A002005532016&coordsys=1&format=shape&datum=2011" target="_blank" rel="noopener">e-stat</a></li> </ul>
How accurate are developers' emotions inferred from social media?
<p>Companion DATA of the paper "How accurate are developers' emotions inferred from social media?" submitted to the Journal of Systems and Software, 2021.</p> <p>The folders contain:</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p><br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p><br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Social media articles dataset - article titles and links
<p>This dataset contains the raw results of topic mapping presented at ngi.https://ngitopics.delabapps.eu. </p> <p>The dataset includes the title and link to the collected article, as well as the location on the map. </p> <p>There is a separate dataset for each analysed map: six umbrella topics + 6 regions</p> <p>For details on data collection and methodology see: https://ngitopics.delabapps.eu/report.pdf and https://ngitopics.delabapps.eu/report_multilanguage.pdf</p> <p> </p> <p> </p>
Survey of Generation Z's Social Media Privacy Awareness
<p>This dataset is the result of a survey regarding "Social Media Privacy Awareness of Generation Z" to 127 students in Institut Teknologi Sepuluh Nopember (ITS) Surabaya</p>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022.</p> <p>The folders contain:</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p><br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results</p> <p> Statistics - Polynomial Regression: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p><br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
A Social and News Media Benchmark Dataset for Topic Modeling
<p>A novel approach to topic modeling using PSO-based clustering is compared to traditional techniques using the 20 Newsgroups dataset and a collection of posts from the Reddit health forum r/Cancer.</p>
Dataset for Report: "The Increasing Prominence of Prejudice and Social Justice Rhetoric in UK News Media"
<p>This data set contains frequency counts of target words in 16 million news and opinion articles from 10 popular news media outlets in the United Kingdom. The target words are listed in the associated report and are mostly words that denote prejudice or are often associated with social justice discourse. A few additional words not denoting prejudice are also available since they are used in the report for illustration purposes of the method.</p> <p>The textual content of news and opinion articles from the outlets is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.</p> <p>Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1 million words of article content from an outlet. This threshold was chosen to maximize inclusion in our analysis of outlets with sparse amounts of articles text per year. </p> <p>Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles.</p> <p>Most of the incorrect frequency counts are often minor deviations from the actual counts such as for instance counting the word "Facebook" in an article footnote encouraging article readers to follow the journalist’s Facebook profile and that the XPath expression mistakenly included as the content of the article main text.To conclude, in a data analysis of over 16 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 2 of main manuscript for supporting evidence of the temporal precision of the method).</p>
Searching for Information on Social Media - An OER on information literacy
<p>This open educational resource video offers digital immigrants and strangers the skills to find authoritative information on social media. With a focus on the ACRL's information literacy framework threshold concepts of "Authority is contextual and constructed" and "Information has value". It is intended for use and re-use in information literacy educational contexts.</p> <p>We have included the video file, a transcript and the slides used to make the OER.</p> <p>This video was made with PowerPoint with final editing touches in DaVinci Resolve 16.</p>
Monitoring the opioid epidemic via social media discussions
<p><strong>The opioid epidemic persists in the United States; in 2020, annual drug overdose deaths increased by 29% to 91,799, including 68,630 opioid-related deaths. The widespread abuse of opioids across geographies and demographics and the rapidly changing dynamics of abuse require reliable and timely information to monitor and address the crisis. Social media platforms include petabytes of participant-generated data that offers a window into the relationship between individuals and their use of drugs. We assessed the utility of Reddit data for public health surveillance, with a focus on the opioid epidemic. We built a natural language processing pipeline to identify opioid-related comments and created a cohort of 1,689,039 geo-located Reddit users, each assigned to a city and state. We followed these users over a period of 10+ years and measured their opioid-related activity over time. We benchmarked the activity of this cohort against CDC overdose death rates for different drug classes and NFLIS drug report rates. Our Reddit-derived rates of opioid discussion strongly correlated with external benchmarks on the national, regional, and city level. During the period of our study, kratom emerged as an active discussion topic; we analyzed mentions of kratom to understand the dynamics of its use. We also examined changes in opioid discussions during the COVID-19 pandemic; in 2020, many opioid classes showed marked increases in discussion patterns. Our work suggests the complementary utility of social media as a part of public health surveillance activities.</strong></p> <p> </p>
Data from: The viewer doesn't always seem to care - response to fake animal rescues on YouTube and implications for social media self-policing policies
<p>Animal-related content on social media is hugely popular but is not always appropriate in terms of how animals are portrayed or how they are treated. This has potential implications beyond the individual animals involved, for viewers, for wild animal populations, and for societies and their interactions with animals. Whilst social media platforms usually publish guidelines for permitted content, enforcement relies at least in part on viewers reporting inappropriate posts. Currently, there is no external regulation of social media platforms. Based on a set of 241 "fake animal rescue" videos that exhibited clear signs of animal cruelty and strong evidence of being deliberately staged (i.e. fake), we found little evidence that viewers disliked the videos and an overall mixed response in terms of awareness of the fake nature of the videos, and their attitudes towards the welfare of the animals involved. Our findings suggest, firstly, that despite the narrowly defined nature of the videos used in this case study, exposure rates can be extremely high (one of the videos had been viewed over 100 million times), and, secondly, that many YouTube viewers cannot identify (or are not concerned by) animal welfare or conservation issues within a social media context. In terms of the current policy approach of social media platforms, our findings raise questions regarding the value of their current reliance on consumers as watch dogs.</p>
Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-Worthiness
<h2>Randomly Sampled Users Dataset (RSU.csv)</h2> <p>This dataset consists of 11,173 users collected through Twitter's APIs. We collected 10,000 random English tweets in February 2023 using Twitter's Volume Stream API. The tweets were posted by around 3,000 users. For each user, we collected up to 100 of its most recent followees using Twitter's Following API. Through the Timeline and Liking APIs, for each user, we collected their most recent tweets (up to 3,200 tweets due to Twitter's limit) and liked-tweets (up to 3,200 too). We then filtered out users that have insufficient tweets (less than 100 original-tweets or less than 80 retweets/liked-tweets) to ensure that the sample sizes are statistically significant in our analyses. Finally, we have 11,173 users along with 40,405,150 tweets. </p> <h2>Humanities Dataset (HUM.csv)</h2> <p>This dataset contains 341,285 tweets and 498 Twitter accounts from selected Twitter lists including <em>Book Author</em>, <em>Christianity</em>, <em>Artists</em>, <em>Buddhism</em>, <em>Musician</em>, and <em>Philosophers</em>. We use Twitter's List and Timeline APIs to collect the accounts and their most recent tweets (up to 1,000). The dataset was collected in January 2024.</p> <h2>Politics Dataset (POL.csv)</h2> <p>This dataset contains all tweets from selected U.S. news media and U.S. politicians including <em>Senators</em>, <em>House Members</em>, <em>US Governors</em>, <em>US Secretaries of State</em>, <em>US Cabinet</em>, and <em>US Election Officials</em> at collection time. We used Twitter's Timeline API to collect the accounts' tweets (up to 3,200 tweets). The dataset was collected in May 2023, with 8,153,745 tweets and 3,784 Twitter accounts. </p> <h2>Data Fields</h2> <p>Due to Twitter's content redistribution policy, we are only allowed to publish tweet IDs and user IDs. Therefore, in each dataset, each row/datapoint represent a tweet, containing two fields --- tweet_id and user_id. </p>
Electric car subsidies in social media and parliament 2022–2023: analysis results
<p>Electric car subsidies in social media and the Finnish parliament 2022–2023: comparative analysis results obtained using three methods for qualitative classification of textual data.</p>
Content analysis of Spanish Defense institutions' NATO eFP social media messages (2017-2022)
<p>Content analysis of Spanish Defense institutions' social media messages about the NATO Enhanced Forward Forward (eFP) mission on Facebook, Twitter (X), Instagram and YouTube (2017-2022).</p>
Incentivizing news consumption on social media platforms using large language models and realistic bot accounts
<p>This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. Treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organization, the sharing/liking of news content and the tweeting/liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control.</p>
Human Microbiome Action - Social Media Templates
<p>This comprehensive resource pack includes ready-to-use messages in PNG format and versatile templates for Twitter/X, LinkedIn, and Instagram available as PowerPoint files. Designed to streamline your communication efforts, these templates enable you to effectively share the latest advancements and insights in human microbiome research with your audience.</p>
Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks
<p>Text mining of scientific libraries and social media has already proven itself as a reliable tool for<br> drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept<br> in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language<br> System (UMLS), is known as medical concept normalization. This task is challenging due to the<br> differences in medical terminology between health care professionals and social media texts coming<br> from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks<br> and semantic representation of one- or multi-word expressions: we develop end-to-end architectures<br> directly tailored to the task, including bidirectional Long Short-Term Memory and Gated Recurrent<br> Units with an attention mechanism and additional semantic similarity features based on UMLS.<br> Our evaluation over a standard benchmark shows that recurrent neural networks improve results<br> over an effective baseline for classification based on convolutional neural networks. A qualitative<br> examination of mentions discovered in a dataset of user reviews collected from popular online health<br> information platforms as well as quantitative evaluation both show improvements in the semantic<br> representation of health-related expressions in social media.</p>
Dataset Questionnaire Social Media Marketing Activities, Brand Loyalty, Brand Trust, Brand Equity, and Industry Fashion In Indonesia
<p>The following dataset is a dataset from a study that investigated Social Media Marketing Activities, Brand Loyalty, Brand Trust, and, Brand Equity in the context of fashion industry in Indonesia.</p>
Indonesian Foreign Policy towards Iran: Shia Hate speech on X social media with LSTM and SVM Analysis
<p>This table and figure are integral components of research on Shia hate speech on social media X, a critical issue in the context of identity politics in Indonesia and globally. This study is of paramount importance as it delves into the identity politics often exploited by politicians in Indonesia and around the world. The Shia community's support for President Jokowi in the first and second stages of the Election was met with hate speech from the opposition group. The study further investigates whether this Shia hate speech is linked to the government's policy towards Iran, a country known for its Shia ideology. The study is presented in three parts:<br>1. Table detailing the sentiment analysis process and results, which were conducted using advanced machine learning techniques such as SVM and LSTM. This approach significantly enhances the accuracy and reliability of the study's findings.<br>2. Figure in the form of a graph related to the study results and the results of comments from the Indonesian public about Shia.<br>3. This research is backed by a comprehensive dataset comprising public comments from Indonesia on Shia. This extensive data collection ensures the study's conclusions are thorough and reliable.</p> <p>4. Processing of machine learning</p>
Reducing social media engagement with affectively polarised content via inoculation
<p>Datasets for validation study and experiments one - six.</p>
Dataset Literatur Review Online Business AND Social Media
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci Online Business AND Social Media</p>
ScienceDex guides
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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.