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ShareScore release 0.9.0
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18 results for “online social networks”
Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities
<p>This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research.</p> <h3>Dataset</h3> <p>The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.</p> <p> </p> <table> <tbody> <tr> <td><strong>Subreddit</strong></td> <td><strong>Posts</strong></td> <td><strong>Comments</strong></td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table> <p> </p> <h3><strong>Annotation proccess</strong></h3> <p>The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as <strong>Toxic</strong>, <strong>Non-toxic</strong>, <strong>I do not know</strong> and <strong>Missing info</strong>. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing. </p> <h3>Available data</h3> <p>The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below.</p> <p>- <strong>id</strong>: The unique identifier of the comment on the Reddit platform<br>- <strong>body</strong>: The original comment text publication<br>- <strong>is_toxic</strong>: The final label of a given comment. The label is <strong>0</strong> for non-toxic comments, <strong>1</strong> for toxic comments and <strong>-1</strong> for comments where the raters disagreed about the toxicity.</p>
Social Network Online Activity of 100+ Users Over Two Years
<p>This dataset contains a precise (error margin is within 5 seconds) activity log of 138 users recorded over a period of approximately two years. It includes users' log in/log off timestamps as well as a device id which was used during the session. An activity heat map is also provided which can be used to determine the online time (in seconds) in a given hour for a given user. The dataset is completely anonymized and is not linked to real peoples' accounts. Russian social network VK was used to record the data.</p> <p>The database is provided in SQLite3 format. The data format is the following:</p> <p><strong>'sessions' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>platform</td> <td>INTEGER</td> <td>Device identifier for the session (refer to the table below).</td> </tr> <tr> <td>time_from</td> <td>DATE</td> <td>Timestamp of the session's start.</td> </tr> <tr> <td>time_to</td> <td>DATE</td> <td>Timestamp of the session's end.</td> </tr> </tbody> </table> <p><strong>'map' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>hour</td> <td>INTEGER</td> <td>Hour from the 1st Jan 1970 (Unix Epoch / 3600).</td> </tr> <tr> <td>time</td> <td>INTEGER</td> <td>Accumulated online time in the hour (in seconds).</td> </tr> </tbody> </table> <p>Device identifiers:</p> <table> <tbody> <tr> <td>0</td> <td>Unknown</td> </tr> <tr> <td>1</td> <td>Web on Mobile </td> </tr> <tr> <td>2</td> <td>iPhone App</td> </tr> <tr> <td>3</td> <td>iPad App</td> </tr> <tr> <td>4</td> <td>Android App</td> </tr> <tr> <td>5</td> <td>Windows Phone App</td> </tr> <tr> <td>6</td> <td>Windows App</td> </tr> <tr> <td>7</td> <td>Web on Desktop</td> </tr> </tbody> </table> <p> </p> <p>This dataset is associated with the VKWatcher independent research project. The code used to gather the information can be found <a href="https://github.com/Azarattum/VKWatcher-Backend">on GitHub</a>.</p>
Facebook and Friends: Developing an Effective Online Social Network for Weight Loss
ClinicalTrials.gov study NCT02656680. IPD Sharing: NO. Countries: 1. Publications: 2.
RCT of an Internet Cessation Program Plus Online Social Network for HIV+ Smokers
ClinicalTrials.gov study NCT02781090. IPD Sharing: NO. Countries: 1. Publications: 3.
Chimpanzee identification and social Network construction through an online citizen science platform
<p><span><span><span><span><span><span><span><span><span><span><span>Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (<i>Pan troglodytes</i>) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists to that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species. </span></span></span></span></span></span></span></span></span></span></span></p>
HOPE (Harnessing Online Peer Education): Using Online Social Networks for HIV Prevention and Testing
ClinicalTrials.gov study NCT01701206. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Testing the Efficacy of an Online Social Network Intervention to Increase Social Support for Physical Activity
ClinicalTrials.gov study NCT01421758. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Improving Health Equity for COVID-19 Vaccination for At-risk Populations Using Online Social Networks
ClinicalTrials.gov study NCT04779827. IPD Sharing: YES. Countries: 1. Publications: 2.
Online Social Networking for HIV Prevention
ClinicalTrials.gov study NCT00725959. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Support Via Online Social Networks to Promote Safe Infant Care Practices
ClinicalTrials.gov study NCT05767658. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Geographies of an online social network
Open the record for dataset details and reuse information.
Chimpanzee identification and social Network construction through an online citizen science platform
Open the record for dataset details and reuse information.
Data from: How the online social networks are used: dialogs-based structure of MySpace
Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors such as user's psychology profiles and their reasons to use the online contacts. In this study, we have compiled and analysed two datasets from MySpace. The data contain networked dialogues occurring within a specified time depth, high temporal resolution and texts of messages, in which the emotion valence is assessed by using the SentiStrength classifier. Performing a comprehensive analysis, we obtain three groups of results: dynamic topology of the dialogues-based networks have a characteristic structure with Zipf's distribution of communities, low link reciprocity and disassortative correlations. Overlaps supporting 'weak-ties' hypothesis are found to follow the laws recently conjectured for online games. Long-range temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion; patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and interactivity times longer than 1 day. Taken together, these results give a new insight into the functioning of online social networks and unveil the importance of the amount of information and emotion that is communicated along the social links. All data used in this study are fully anonymized.
Mobile-based Online Social Network Intervention to Increase Physical Activity
ClinicalTrials.gov study NCT02736903. IPD Sharing: NO. Countries: 0. Publications: 3.
Data from: How the online social networks are used: dialogs-based structure of MySpace
Open the record for dataset details and reuse information.
Mindfulness Intervention and Online Social Networking
ClinicalTrials.gov study NCT06090760. IPD Sharing: Not stated. Countries: 1. Publications: 0.
RCT for Evaluation of Online Social Networking Intervention in Smoking Cessations
ClinicalTrials.gov study NCT03387800. IPD Sharing: NO. Countries: 1. Publications: 0.
A Survey on the Role and Benefits of Online Social Networks on Filipino Patients With Psoriasis
ClinicalTrials.gov study NCT01465061. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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
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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.