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434 results for “Social Media”
Hormone Replacement Therapy Social Media Claims
<p>The claims made in social media posts about hormone replacement therapy, the tpye of person/organisation posting these claims, whether the claims agree with NICE/BNF guidance and whether the person/organisation posting these claims has a conflict of interest. Full unredacted dataset available on request to: <a href="mailto:mm494@st-andrews.ac.uk">mm494@st-andrews.ac.uk</a></p>
Detection of Real-World Influence through Social Media
<p><strong>Description. </strong>This dataset corresponds to the resources produced for the following conference paper and its extended version:</p> <ol> <li>J.-V. Cossu, N. Dugué, and V. Labatut, “Detecting Real-World Influence Through Twitter,” in <em>2nd European Network Intelligence Conference (ENIC)</em>, 2015, pp. 83–90. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01164453">hal-01164453</a>⟩ DOI: <a href="http://doi.org/10.1109/ENIC.2015.20">10.1109/ENIC.2015.20</a></li> <li>J.-V. Cossu, V. Labatut, and N. Dugué, “A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence,” <em>Social Network Analysis and Mining </em>6:25, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01203171">hal-01203171</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-016-0329-x">10.1007/s13278-016-0329-x</a></li> </ol> <p>Raw data are available through the official RepLab page: <a href="http://nlp.uned.es/replab2014/">http://nlp.uned.es/replab2014/</a> (follow <a href="http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz">http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz</a>)</p> <p><strong>Source code. </strong>The source code used to generate these output is available on GitHub: <a href="https://github.com/CompNet/Influence">https://github.com/CompNet/Influence</a></p> <p><strong>Funding. </strong>This work was partly funded by the French National Research Agency (ANR), through the project <a href="https://anr.fr/Project-ANR-12-CORD-0002">ImagiWeb ANR-12-CORD-0002</a>.</p> <p><strong>Contact. </strong>Jean-Valère Cossu <<a href="mailto:jean-valere.cossu@alumni.univ-avignon.fr">jean-valere.cossu@alumni.univ-avignon.fr</a>></p> <p><strong>Citation. </strong>If you use these data, please cite paper [1] above.</p> <p><br><code>@InProceedings{Cossu2015,</code><br><code> author = {Cossu, Jean-Valère and Dugué, Nicolas and Labatut, Vincent},</code><br><code> title = {Detecting Real-World Influence Through {Twitter}},</code><br><code> booktitle = {2\textsuperscript{nd} European Network Intelligence Conference},</code><br><code> year = {2015},</code><br><code> pages = {83-90},</code><br><code> address = {Karlskrona, SE},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ENIC.2015.20},</code><br><code>}</code></p> <p><strong>Details. </strong>This archive contains all ranking outputs formatted according to the TREC-EVAL tool format. These outputs consist for each domain in a ranked list of user from the most influential to the least influential. For a classification-type evaluation, just consider that users having a score higher than 0.5 are influential.</p> <p>File names correspond to the system (those starting with Cos*, indicate: the method BoT for Bag-of-Tweets, UaD for User-as-Document; the use of the Tweet-Selection strategy files denoted Artex; the learning process with Global or separated models which are noted Multi and last but not least the decision strategy for Bag-of-Tweets: Counting or Sum) or feature name. Files starting with out_* contain the results of logistic regression ranking outputs. Files matrix_auto.dat and matrix_bank.dat contain the data used to feed the PLS model (code: plspm4influence.R).</p> <p>RepLab 2014 uses Twitter data in English and Spanish. The balance between both languages depends on the availability of data for each of the profiles included in the dataset.</p> <p>The training dataset consists of 7,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains, evaluation is performed separately. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time and have been manually labelled by reputation experts either as “opinion maker” (i.e. authors with reputational influence) or “non-opinion maker”. The objective is to find out which authors have more reputational influence (who the opinion makers are) and which profiles are less influential or have no influence at all. </p> <p>Since Twitter ToS do not allow redistribution of tweets, only tweets ids and screen names are provided. Replab organizers provide details about how to download the tweets.</p>
Cross-Lingual Dataset of Crisis-Related Social Media
<p>The cross-lingual natural disaster dataset includes public tweets collected using Twitter’s public API, filtering by location-related keywords and date, without using any additional filtering (e.g., we did not restrict the query to specific languages). We considered two disaster events and two long-term natural disasters across Europe (floods and wildfires) that received substantial news coverage internationally.</p> <p>Three of the top languages were common to all the studied events: English (ISO 639-1 code: en), Spanish (es), and French (fr). Additionally, we found hundreds of messages for each event in other five languages, including Arabic (ar), German (de), Japanese (ja), Indonesian (id), Italian (it) and Portuguese (pt). </p> <p>After collecting the data, we labelled tweets that contained potentially informative factual information. We name this group of tweets “informative messages.” Next, we used crowdsourcing to further categorize the messages into various informational categories. We asked three different workers to label each informative messages across languages. The target categories were based on an ontology from TREC-IS 2018, where we grouped some low level ontology categories into higher-level ones.</p>
Detecting East Asian Prejudice on Social Media
<p>This repository contains:</p> <ul> <li>A deep learning model which distinguishes between Hostililty against East Asia, Criticism of East Asia, Discussion of East Asian prejudice and Neutral content. The F1 score is 0.83.</li> <li>A detailed annotation codebook used for marking up the tweets.</li> <li>A labelled dataset with 20,000 entries.</li> <li>A dataset with all 40,000 annotations, which can be used to investigate annotation processes for abusive content moderation.</li> <li>A list of thematic hashtag replacements.</li> <li>Three sets of annotations for the 1,000 most used hashtags in the original database of COVID-19 related tweets. Hashtags were annotated for COVID-19 relevance, East Asian relevance and stance.</li> </ul> <p>The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. This data repository is for a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic.</p> <ul> </ul> <p>This work is a collaboration between The Alan Turing Institute and the Oxford Internet Institute. It was funded by the Criminal JusticeTheme of the Alan Turing Institute under Wave 1 of The UKRI Strategic Priorities Fund, EPSRC Grant EP/T001569/1</p>
SOCIAL MEDIA DATA 3 SPORTING EVENTS
<p>Comments on social networks (Facebook, Instagram, Twitter and YouTube) about the brand Spain linked to three chosen sporting events (mega, medium and<br>local): a football mega event (Qatar Football World Cup), a semi-massive tennis event (Davis Cup 2022) and a local marathon event (XLI<br>Marathon Valencia Trinidad Alfonso 2022</p>
Social Media Tweets Pro and Anti Bolsonaro
<p><strong>Overview</strong></p> <p>Social media platforms have an important role in Brazilian society's polarization. Especially during the COVID-19 pandemic (2020-2021), these platforms have a peak of posts about President Bolsonaro's speech and behavior during this period. On the one hand, millions of people support Bolsonaro's attitudes and follow his controversial guidances. On the other hand, a vast number of social media users accuse Bolsonaro of acting against democracy and science. </p> <p>In this context, this dataset presents the collection of tweets posts and its linked news articles (including all related media) covering two Brazilians’ demonstrations events PRO and AGAINST President Bolsonaro government, during September 7th and October 2nd of 2021.<br> The dataset contains 4.7M tweets.</p> <p><strong>Data Collection</strong><br> We use the library Fake-News Crawler (https://github.com/phillipecardenuto/fakenews-crawler) to collect the tweet posts and related media. For this, we provided keywords related to both events to receive the data during events and following days. For instance, some of the keywords used were ‘<em>7deSet</em>’, ‘<em>BolsonaroAte2026</em>’, ‘<em>VemParaRua</em>’, ‘<em>EleNao</em>’, ‘<em>07EuVou</em>’, ‘<em>SupremoÉOPovo</em>’, '<em>aculpaédobolsonaro</em>’, ‘<em>2outeuvou</em>’.<br> <br> <strong>Disclaimer</strong>: We did not perform any filtering or procedure to assert that all collected data is, in fact, related to the demonstrations; therefore, some of the content of the dataset might not be related to these events.</p> <p><strong>Content</strong><br> brazilian_demonstration_events.json: It contains the tweet posts, their metadata (e.g., post time, language), and all related media content URLs (i.e., news article link and media links).<br> <br> <strong>Media Content</strong><br> Due to the terms of use from the social networks, we do not make publicly available the images and videos that were collected. However, we can provide some extra pieces of media content related to one (or more) events by contacting the authors.<br> <br> <strong>Funding</strong><br> DéjàVu thematic project, São Paulo Research Foundation (grants 2017/12646-3, 2020/02241-9 and 2020/02211-2)<br> </p>
Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"
<pre>These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper "Universality, criticality and complexity of information propagation on social media". The analysis is reported in <a href="https://arxiv.org/abs/2109.00116">https://www.nature.com/articles/s41467-022-28964-8</a> Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th. </pre>
Social Media Mask Dataset
<p>The Social Media Mask Dataset is a dataset made up of Twitter Images intended for the training of Convolutional Neural Networks to detect masks in images and video. In this case, the term masks refer to a device worn on the face intended to reduce the spread of respiratory illness. Due to Twitter's TOS, we cannot directly publish Twitter images. Instead, we publish Tweet keys along with information our script uses to download the target image. The file training.json is our suggested training set while testing.json is our suggested test set. Our script download_dataset.py can be used to download the full dataset with a Twitter developer account. </p>
Methods of promoting modern theater on social media / Методи популяризації сучасного театру у соціальних мережах
<p>The dataset "Methods of promoting contemporary theatre on social media", based on a survey of 105 respondents, includes answers to the following questions:</p> <ul> <li>How often do you go to the theatre?</li> <li>What social media do you use to find out about theatre events?</li> <li>What type of content on social media is most effective in drawing your attention to theatre events? - Has social media ever prompted you to buy theatre tickets?</li> <li>What factors influence your decision to attend a theatre performance you saw on social media?</li> <li>How do you assess the overall effectiveness of social media in promoting theatre events?<br>Data downloaded in .csv format.</li> </ul>
RedMed: Extending drug lexicons for social media applications
<p>Data associated with the RedMed project.</p> <p>Details for the process behind the data creation can be found in the associated paper:</p> <p><strong>Lavertu, A. & Altman, R. B. </strong>"RedMed: Extending drug lexicons for social media applications"<br> Journal of Biomedical Informatics, (2019)</p> <p><a href="https://doi.org/10.1016/j.jbi.2019.103307">https://doi.org/10.1016/j.jbi.2019.103307</a></p> <p><strong>RedMed embedding model:</strong></p> <p>Word vectors trained on comments from health related subreddits and optimized for drug synonym retrieval.</p> <p>The Redmed model was train using only social media data from Reddit and achieves comparable performance on the UMNSRS and MayoSRS similarity tasks. Vectors are 64 dimensional.</p> <p><strong><strong>redmed_model_vectors.tsv.gz - </strong></strong>Tab-separated word vectors (token\tdim1\tdim2\t...dim64)</p> <p><strong><strong>redmed_model.bin - </strong></strong>Binary word2vec file saved using gensim, can be loaded into python gensim</p> <p>Other Files:</p> <p>supp_file_1_sidebar_subreddits.txt - List of health-related subreddits based on "r/Health" and "r/Drugs" sidebars<br> supp_file_2_enrichment_based_subreddits.txt - List of health-related subreddits based on amount of health-related content<br> supp_file_3_custom_stopword_list.txt - List of stopwords based on counts derived from Reddit comments</p>
AI validated plant observations from social media: Flickr images from central London 2011-2019
<p>This dataset is the result of using an AI image classifer to classify images of plants on social media. We believe this is the first AI validated dataset of biological records taken from social media. This represents the dawn of AI naturalists whose domain of exploration is not the outdoor world but the digital realm. These AI naturalists will trawl streams of data from all over the globe, identifying genuine images of species, and in so doing create valuable data sets that will further our understanding of the distribution of wildlife on our planet. </p> <p>This dataset contains 31,973 classifications of images taken in central London between May 2011 and September 2019 retrieved using the search term 'flower' on Flickr.com. Some images have very low classification confidence (7910 below 0.1), while others have very high confidence (3185 over 0.9). As expected given the spatial extent of the dataset many of the observations are of planted species in gardens and parks.</p> <p>August_et_al_2019.csv provides the data while metadata.txt contains a description of the data and its generation.</p> <p>An interactive visualisation of this data can be viewed at <a href="https://tomaugust.shinyapps.io/ai_flickr_data/">https://tomaugust.shinyapps.io/ai_flickr_data/</a></p>
A Global Database of Historic and Real-time Flood Events based on Social Media
<p>Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on <a href="http://www.globalfloodmonitor.org">www.globalfloodmonitor.org</a>.</p>
Cross-Lingual Dataset of Crisis-Related Social Media
<p>The cross-lingual natural disaster dataset includes public tweets collected using Twitter’s public API, filtering by location-related keywords and date, without using any additional filtering (e.g., we did not restrict the query to specific languages). We considered five disaster events between January 2020 and February 2021 that received substantial news coverage internationally.</p> <p>All messages include a “language” field computed by Twitter us ing a language detection model developed specifically for tweets. We counted the number of messages per language in each event. Three of the top languages were common to all the studied events: English (ISO 639-1 code: en), Spanish (es), and French (fr). Additionally, we found several hundred messages for each event in other languages, including Catalan (ca), Tagalog (tl), Croatian (hr), German (de), Japanese (ja), Indonesian (id), and Portuguese (pt). </p> <p>After collecting the data, we labelled tweets or their translation to English that contained potentially informative factual information. We name this group of tweets “informative messages.” Next, we used crowdsourcing to further categorize the messages into various informational categories. We asked three different workers to label each of the approximately 5,700 informative messages across languages. The target categories were based on an ontology from TREC-IS 2018, where we grouped some low level ontology categories into higher-level ones.</p>
Dataset for Evaluating Abstractive Summaries of Crisis-Related Social Media
<p>The dataset created for evaluation of summaries generated from social media posted during five natural disasters.</p> <p>The dataset contains:</p> <ul> <li> <p>ground truth reports created by human assessor based on ERCC Echo Flash reports (5 events);</p> </li> <li> <p>summaries generated by extractive and abstractive state-of-the-art with manual annotation of category-relevant and crisis-relevant claim.</p> </li> </ul> <p>The dataset with annotated social media postings — <a href="https://doi.org/10.5281/zenodo.7714014">link</a></p> <p>The full description of used summarization methods, sources of data and evaluation metrics available in the paper — <a href="https://dl.acm.org/doi/abs/10.1145/3511095.3531279">link</a></p>
Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages
<p>Data and supplementary information for the paper entitled "Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages" to be published at EMNLP 2015: Conference on Empirical Methods in Natural Language Processing — September 17–21, 2015 — Lisboa, Portugal.</p> <p>ABSTRACT: Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e. text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.</p>
EduQuick: A Dataset for Assessing Summarization of Informal Educational Content for Social Media
<p>The presented dataset is a curated collection of model-generated text for educational TikTok content, abbreviated as EduQuick. This dataset is the result of evaluating and selecting high-quality content generated by the GPT-4 model following an empirical study. It aims to provide engaging and informative summaries suitable for TikTok's educational audience.</p>
BIObec's social media analytics
<p>In this folder can be found all the LinkedIn and X (formerly Twitter) analytics from the BIObec project's accounts, since beginning of the project. </p>
Predators of Japanese myriapods — survey using literature, social media, Web, and mobile application —
<p>I provide a database reviewing information on predators of Japanese myriapods. The information within this database was compiled from published accounts in Japan along with visual media accumulated on the Internet.</p>
InnoRate_Social_media_statistics_Dataset14_2021.12.28_v1
<p>This dataset contains the final statistics of (i) the social media accounts (Facebook, Twitter, LinkedIn) and (ii) the InnoRate web portal, which both have been created in the frame of the InnoRate Project (H2020 GA 821518).</p>
Datasample - Social Media Analytics and Metrics of Facebook Performance of Libraries, Archives and Museums
<p>The current dataset describes Facebook pages performance for 220 Libraries, Archives and Museums from all over the world. The performance is measured through 9 different social media metrics. That is, number of posts, link-posts, picture-posts, video-posts, total reactions, comments and shares, number of reactions, comments per post and reactions per post. The data harvesting process has been conducted through the use of FanPageKarma API. The gathered metrics and their values depict the performance for each Facebook page in a time-period of 30 days.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.