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434 results for “Social media”
A Gamified, Social Media Inspired Personalized Normative Feedback Alcohol Intervention for Sexual Minority Women
ClinicalTrials.gov study NCT03884478. IPD Sharing: YES. Countries: 1. Publications: 2.
ACT-Based Psychoeducation for Youth With Social Media Addiction
ClinicalTrials.gov study NCT07343180. IPD Sharing: NO. Countries: 1. Publications: 14.
Assessing the Impact of a Social Media-Based Educational Intervention Using WhatsApp Video Messages on Dental Caries Prevention Knowledge, Oral Hygiene Practices, and Attitudes Toward Dental Health Am
ClinicalTrials.gov study NCT07363317. IPD Sharing: NO. Countries: 1. Publications: 3.
A Music and Visual Arts Digital Intervention in Teenagers to Promote Healthy Engagement With Social Media.
ClinicalTrials.gov study NCT06402253. IPD Sharing: YES. Countries: 1. Publications: 22.
Social Media and COVID-19
ClinicalTrials.gov study NCT04367363. IPD Sharing: NO. Countries: 1. Publications: 1.
Social Media Intervention for Cannabis Use in Emerging Adults
ClinicalTrials.gov study NCT04187989. IPD Sharing: NO. Countries: 1. Publications: 1.
Social media occurrences of the Banded Demoiselle in Great Britain
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Incentivizing news consumption on social media platforms using large language models and realistic bot accounts
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Reranking partisan animosity in algorithmic social media feeds alters affective polarization
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Data from: Social media highlights the overlooked impact of cats on arthropods
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Data From: Analysing social media forums to discover potential causes of phasic shifts in cryptocurrency price series
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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
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Data file: a qualitative social media content analysis of the Dutch #breakthesilence campaign on negative and traumatic experiences of labour and birth
<p>Data file with typed out quotes from the Dutch #breakthesilence campaign analysed for the study 'Left powerless: a qualitative social media content analysis of the Dutch #breakthesilence campaign on negative and traumatic experiences of labour and birth'.</p>
Data from: Understanding sentiment of national park visitors from social media data
<p>National parks are key for conserving biodiversity and supporting people´s well-being. However, anthropogenic pressures challenge the existence of national parks and their conservation effectiveness. Therefore, it is crucial to assess how people perceive national parks in order to enhance socio-political support for conservation. User-generated data shared by visitors on social media provide opportunities to understand how people perceive (e.g. preferences, feelings, opinions) national parks during nature-based recreational experiences. In this study, we applied methods from automated natural language processing to assess visitors' sentiment when describing experiences in Instagram posts geolocated inside four national parks in South Africa. We found that visitors' sentiment was positive, and mostly included emotions such as joy, anticipation, trust and surprise, with only a small occurrence of posts with negative feelings. Appreciation of nature, in association with a diverse set of other aspects, such as activities, geographical features and tourist attractions, was used to describe experiences related to nature, wilderness, traveling, holidays and adventures. The type of nature-based experience described by visitors was park specific, revealing different profiles of parks providing wildlife or scenery experiences. Findings support and highlight the societal role of national parks in providing visitors with opportunities to develop positive connections with nature. Social media data may be used to understand visitors' perceptions, and how the image of national parks is constructed by users in the virtual social environment. This may help inform management for promoting a high quality tourism experience, as well as conservation marketing aimed at fostering socio-political support for national parks and their long-term conservation effectiveness.</p>
poojaruhal/RP-commenting-practices-social-media: RP-commenting-practices-social-media: RP_TOSEM_2020 v.1.0.1 Second release of of the replication Package for the paper "What do Developers Discuss about Code Comment Conventions on Social Media"
<p>RP-commenting-practices-social-media</p> <p>Replication Package for the paper "What do Developers Discuss about Code Comment Conventions on Social Media?"</p> <p>Structure</p> <pre><code>Paper-presenation.pdf Makar_tool/ Data/ stackoverfow_questions_with_answers_by_tags.csv stackoverfow_tags_metrics.csv apache_mailing_list.csv mailing_lists_ASF_@dev_@users_1.csv mailing_lists_ASF_@dev_@users_2.csv quora.csv sample_stackoverfow_questions_with_answers_by_tags.csv Schemas/ apache_mailing_lists.json quora.json stackoverfow_questions_answers_by_tag.json stackoverfow_tag_count.json stackoverfow_tag_metrics.json RQ1/ LDA_input/ stackoverfow_raw_dataset.csv LDA_output/ Mallet/ output_csv/ docs-in-topics.csv topic-words.csv topics-in-docs.csv topics-metadata.csv output_html/ all_topics.html Docs/ Topics/ RQ2/ datasource_rawdata/ mailing_lists_selection_criteria.csv quora.csv stackoverflow.csv manual_analysis_output/ stackoverflow_quora_taxonomy.xlsx </code></pre> <p>Contents of the Replication Package</p> <p><strong>Paper-presenation.pdf</strong> presents the highlights of the work in a presenation.</p> <p><strong>Makar_tool/</strong> contains the data processed using the tool for the study</p> <ul> <li> <p><strong>Data/</strong></p> <ul> <li><code>stackoverfow_questions_with_answers_by_tags.csv</code> - all stackoverflow questions used in the study as stored in Makar <ul> <li><code>stackoverfow_tags_metrics.csv</code> - all data containing the calculations done for stackoverflow tag selection</li> <li><code>apache_mailing_list.csv</code> - statistically significant sample of <code>mailing_lists_ASF_@dev_@users_1.csv</code> and <code>mailing_lists_ASF_@dev_@users_2.csv</code> used in the study</li> <li><code>mailing_lists_ASF_@dev_@users_1.csv</code> - mailing list data used in the study as stored in Makar (part 1)</li> <li><code>mailing_lists_ASF_@dev_@users_2.csv</code> - mailing list data used in the study as stored in Makar (part 2)</li> <li><code>quora.csv</code> - all quora questions used in the study as stored in Makar</li> <li><code>sample_stackoverfow_questions_with_answers_by_tags</code> - statistically significant sample of <code>stackoverfow_questions_with_answers_by_tags.csv</code> used in the study</li> </ul> </li> </ul> </li> <li> <p><strong>Schemas/</strong></p> <ul> <li><code>apache_mailing_lists.json</code> - data schema used in Makar to store mailing list data</li> <li><code>quora.json</code> - data schema used in Makar to store quora data</li> <li><code>stackoverfow_questions_answers_by_tag.json</code> - data schema used in Makar to store stackoverflow questions data</li> <li><code>stackoverfow_tag_count.json</code> - data schema used in Makar to lookup number of questions per tag available in stackoverflow</li> <li><code>stackoverfow_tag_metrics.json</code> - data schema used in Makar to stackoverflow tag metrics data</li> </ul> </li> <li> <p><strong>RQ1/</strong> - contains the data used to answer RQ1</p> <ul> <li><strong>LDA_input/</strong> - input data used for LDA analysis <ul> <li><code>stackoverfow_raw_dataset.csv</code> - stackoverflow questions used to perform LDA analysis</li> </ul> </li> <li><strong>LDA_output/</strong> <ul> <li><strong>Mallet/</strong> - contains the LDA output generated by MALLET tool <ul> <li><strong>output_csv/</strong> <ul> <li><code>docs-in-topics.csv</code> - documents per topic</li> <li><code>topic-words.csv</code> - most relevant topic words</li> <li><code>topics-in-docs.csv</code> - topic probability per document</li> <li><code>topics-metadata.csv</code> - metadata per document and topic probability <ul> <li><strong>output_html/</strong> - Browsable results of mallet output</li> </ul> </li> <li><code>all_topics.html</code></li> <li><code>Docs/</code></li> <li><code>Topics/</code></li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li> <p><strong>RQ2/</strong> - contains the data used to answer RQ2</p> <ul> <li><strong>datasource_rawdata/</strong> - contains the raw data for each source <ul> <li><code>mailing_lists_selection_criteria.csv</code> - criteria used to select mailing_lists.</li> <li><code>quora.csv</code> - contains the processed dataset (like removing HTML tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.</li> <li><code>stackoverflow.csv</code> - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.</li> </ul> </li> <li><strong>manual_analysis_output/</strong> <ul> <li><code>stackoverflow_quora_taxonomy.xlsx</code> - contains the classified dataset of stackoverflow and quora and description of taxonomy. <ul> <li><code>Taxonomy</code> - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by <code>|</code> symbol.</li> <li><code>stackoverflow-posts</code> - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. <ul> <li><code>quota-posts</code> - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Use of social media in the marketing of agricultural products and farmers' turnover in South-South Nigeria
<p class="CxSpFirst"><span>The study investigated the use of social media in the marketing of agricultural products and farmers turnover in South-South Nigeria. The purpose of the study was to determine the extent to which the usage of social media in the marketing of agricultural products in Nigeria can enhance efficiency and farmers' sales turnover. It employed the survey research design and data were collected with the help of a structured questionnaire. Research data were analysed using t-test and least square method. The results showed that the use of social media (Facebook, WhatsApp and Instagram) in the marketing of agricultural products enhances efficiency and turnover of farmers through a significant reduction in the cost of marketing agricultural products as well as increased awareness and the attendant increase in demand for agricultural produce.</span></p>
PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media
<p>A Russian-language sentiment lexicon for social media discussions on political and social issues.</p> <p>The file contains raw markings collected with LINIS coding service <a href="https://linis-crowd.org">https://linis-crowd.org</a> [in Russian].</p> <p>Learn more about PolSentiLex in our papers:</p> <ul> <li> <p>Koltsova, O., & Alexeeva, S. (2015). Linis-crowd.org: A lexical resource for Russian sentiment analysis of social media [Linis-crowd.org: Lexichesk resurs dl’a analiza tonal’nosti sotsial’no-politicheskix tekstov]. Computational Linguis- Tics and Computantional Ontologies: Proceedings of the XVIII Joint Conference “Internet and Modern Society (IMS-2015)” [Kompyuternaya Lingvistika i Vyichis- Litelnyie Ontologii: Sbornik Nauchnyih Statey. Trudyi XVIII Ob’edinennoy Konferen- Tsii «Internet i Sovremennoe Obschestvo» (IMS-2015)], 25–34. [in Russian] URL: <a href="https://scila.hse.ru/data/2020/06/02/1603986481/koltsovaoyuetal.pdf">https://scila.hse.ru/data/2020/06/02/1603986481/koltsovaoyuetal.pdf</a></p> </li> <li> <p>Koltsova, O., Alexeeva, S., & Koltsov, S. (2016). An Opinion Word Lexicon and a Training Dataset for Russian Sentiment Analysis of Social Media. Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2016”, 277–287. URL: <a href="http://www.dialog-21.ru/media/3400/koltsovaoyuetal.pdf">http://www.dialog-21.ru/media/3400/koltsovaoyuetal.pdf</a></p> </li> <li> <p>Koltsova O., Alexeeva S., Pashakhin S., Koltsov S. (2020) PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media. In: Filchenkov A., Kauttonen J., Pivovarova L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science, vol 1292. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-59082-6_1">https://doi.org/10.1007/978-3-030-59082-6_1</a></p> </li> </ul>
Utilizing Social Media for Identifying Drug Addiction and Recovery Intervention
<p>This dataset is for the paper "Utilizing Social Media for Identifying Drug Addiction and Recovery Intervention" published in Data Analytics for Smart Health (DASH) workshop (<a href="https://sites.google.com/view/ieee-dash-2020">https://sites.google.com/view/ieee-dash-2020</a>) co-located with the IEEE BigData 2020 conference.</p> <p><strong>Overview of the Paper</strong></p> <p>Topic-specific social media forums such as Reddit have become popular platforms for users discussing health-related information as well as for scientific analysis of that information. Such discussions among users have been found to be effective in providing useful insights and assistance in many healthcare applications. This study focuses on one such application, where we utilize Reddit posts related to drug addiction/substance abuse, in order to help the addicted persons. We observe some linguistic differences in the posts as users gradually move from the addicted stage to more and more advanced recovery stages. We then classify user-posts on Reddit as to be indicative of drug addiction or of different stages of recovery of the user. By annotating Reddit posts with the help of standard social and health psychology literature, we develop a Machine Learning classifier based on linguistic features, to classify the posts among different classes related to addiction and recovery. Finally, we identify users having an intention to recover and perform a personalized mentor recommendation by referring them to users who are already in their advanced stage of recovery based on the usage of drugs. To our knowledge, this work is the first attempt to utilize social media for helping addicted users having the intention to recover with personalized mentor recommendations to facilitate their process of recovery.</p> <p><strong>Overview of the dataset </strong></p> <p>The dataset contains posts from drug addiction-related Subreddits which are topic-specific communities within the Reddit OSM (Online Social Media). We considered 100 distinct Subreddits related to drug addiction and recovery, which are listed in the Wiki page of the Subreddit `Drugs' (<a href="https://www.reddit.com/r/Drugs/wiki/subreddits">https://www.reddit.com/r/Drugs/wiki/subreddits</a>). We annotated 3151 posts out of all the posts collected as one of the 5 classes: 'Addicted', ' E(early)-Recovery', ' M(maintaining)-Recovery', ' A(advanced)-Recovery', 'Others'. </p> <p>The dataset has the following 8 fields, </p> <p>* id: A unique identifier for each post<br> * username: The username for each post<br> * title: Each post is associated with a title<br> * body: The main descriptive part of the post is identified by its 'body'<br> * #comments: This field represents the number of comments the post has received<br> * score: This field represents the number of upvotes received by each post<br> * label_classification: The label given to a post for the classification task (one of the 5 classes stated above)<br> * label_recommendation: The label given to a post for the recommendation task where the 'Addicted' class of classification task was further divided into 'Addicted with intention to recover', and 'Addicted without intention to recover'</p> <p>Note that the dataset has been anonymized by replacing all personally identifiable information (PII) such as usernames, email-ids, phone numbers, SSNs, etc. by using the tool available at <a href="https://github.com/mns-llc/bitsnarf">https://github.com/mns-llc/bitsnarf</a> as well as using some standard dictionary-based methods.</p> <p> </p>
FIGURE 1 in Ontogeny of an arlequin: morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816) (Decapoda: Palaemonidae), traced through citizen science and social media data mining
FIGURE 1. Morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816). A–C. Specimens from Capo Noli (Italy, Mediterranean Sea) (~44.199232N, 8.420455E), 15–16 m, on anthropogenic debris laying on a detritic bottom, 5–13.IX.2020. A. Photo by Walter Bassi. B–C. Photos by Alessandro Raho. D. Specimen from La Laja beach, Gran Canaria (Spain, Atlantic Ocean) (~28.060335N, -15.418428E), 1 m, amidst algae in a tide pool, 29.VIII.2017. Photo by Alberto Navarro. E. Specimen from Bat Galim reef, Haifa (Israel, Mediterranean Sea) (~32.833317N, 34.97431E), 2 m, under a rock on a rocky bottom, ~2017. Photo by Sarah Ohayon. F. Specimen from Capo Caccia, Sardinia (Italy, Mediterranean Sea) (~40.565506N, 8.165579E), 5 m, detritic bottom with rocks, 28.VIII.2015. Photo by Marco Colombo.
"Social Media irritieren viele, weil sich die Spielformen dauernd weiterentwickeln"
<p>Social Media-Kanäle eröffnen neue Möglichkeiten für crossmediales Storytelling. Die beiden Experten Prof. Dr. Axel Vogelsang und Michael Schär haben sich im Café «Si o No» im Zürcher Kreis 5 bei einem Morgenkaffee über die Spielregeln unterhalten.</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
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.