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29 results for “online media”
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Dataset: Sentiment Analysis annotation of News headlines covering the Olympic legacy of Rio 2016 and London 2012 published by the Brazilian and British online media
<p>Dataset of 464 news headlines with sentiment manually annotated by a domain expert using the labels positive, negative and neutral. Data contains URLs for news articles published between 2004-2020 by the British and Brazilian media in English and Brazilian Portuguese covering the Olympic legacies of London 2012 and Rio 2016. Articles were collected from the news outlets’ websites using Google search engine.</p> <p>News outlets:</p> <ul> <li>The Guardian</li> <li>Daily Mail</li> <li>Globo</li> <li>Estadao</li> </ul>
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17
Evaluation Study of the Online High School Media Aware Program
ClinicalTrials.gov study NCT04035694. IPD Sharing: NO. Countries: 1. Publications: 1.
Social media data from online discourse on Nigeria's budget
<p>The social media data was obtained by extracting online discourse on Nigeria's 2013 budget. This was done as a component of the media analysis on the case study investigating the use of the online national budget of Nigeria which is part of the 'Exploring the Emerging Impacts of Open Data in Developing Countries' (ODDC) research project.</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>
Using Social Media to Deliver HIV Self-Testing Kits and Link to Online PrEP Services
ClinicalTrials.gov study NCT04155502. IPD Sharing: YES. Countries: 1. Publications: 2.
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
<p><strong>Motivation</strong></p> <p>The rise of online media has enabled users to choose various unethical and artificial ways of gaining social growth to boost their credibility (number of followers/retweets/views/likes/subscriptions) within a short time period. In this work, we present ABOME, a novel data repository consisting of datasets collected from multiple platforms for the analysis of blackmarket-driven collusive activities, which are prevalent but often unnoticed in online media. ABOME contains data related to tweets and users on Twitter, YouTube videos, YouTube channels. We believe ABOME is a unique data repository that one can leverage to identify and analyze blackmarket based temporal fraudulent activities in online media as well as the network dynamics.</p> <p><strong>License</strong></p> <p>Creative Commons License.</p> <p><strong>Description of the dataset</strong></p> <p>In this work, we focused on collecting data from credit-based freemium services. We divide the datasets into two parts:</p> <p><strong>- Historical Data (</strong><strong>historical_anon.zip</strong><strong>)</strong></p> <p>This consists of all the data for Twitter and YouTube from blackmarket services gathered via sequential querying of the website’s URLs between the period March-June, 2019. We collected the metadata of each entity present in the historical data.</p> <p><strong>Twitter:</strong></p> <p>We collected the following fields for retweets and followers on Twitter:</p> <p><code>user_details</code>: A JSON object representing a Twitter user.</p> <p><code>tweet_details</code>: A JSON object representing a tweet.</p> <p><code>tweet_retweets</code>: A JSON list of tweet objects representing the most recent 100 retweets of a given tweet.</p> <ol> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object</a><a href="#fnref1">↩︎</a></p> </li> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object</a><a href="#fnref2">↩︎</a></p> </li> </ol> <p><strong>YouTube:</strong></p> <p>We collected the following fields for YouTube likes and comments:</p> <p><code>is_family_friendly:</code> Whether the video is marked as family friendly or not.</p> <p><code>genre:</code> Genre of the video.</p> <p><code>duration:</code> Duration of the video in ISO 8601 format (duration type). This format is generally used when the duration denotes the amount of intervening time in a time interval.</p> <p><code>description:</code> Description of the video.</p> <p><code>upload_date:</code> Date that the video was uploaded.</p> <p><code>is_paid:</code> Whether the video is paid or not.</p> <p><code>is_unlisted:</code> The privacy status of the video, i.e., whether the video is unlisted or not. Here, the flag <em>unlisted</em> indicates that the video can only be accessed by people who have a direct link to it.</p> <p><code>statistics:</code> A JSON object containing the number of dislikes, views and likes for the video.</p> <p><code>comments:</code> A list of comments for the video. Each element in the list is a JSON object of the text (<em>the comment text</em>) and time (<em>the time when the comment was posted</em>).</p> <p>We collected the following fields for YouTube channels:</p> <p><code>channel_description:</code> Description of the channel.</p> <p><code>hidden_subscriber_count:</code> Total number of hidden subscribers of the channel.</p> <p><code>published_at:</code> Time when the channel was created. The time is specified in ISO 8601 format (YYYY-MM-DDThh:mm:ss.sZ).</p> <p><code>video_count:</code> Total number of videos uploaded to the channel.</p> <p><code>subscriber_count:</code> Total number of subscribers of the channel.</p> <p><code>view_count:</code> The number of times the channel has been viewed.</p> <p><code>kind:</code> The API resource type (e.g., <em>youtube#channel</em> for YouTube channels).</p> <p><code>country:</code> The country the channel is associated with.</p> <p><code>comment_count:</code> Total number of comments the channel has received.</p> <p><code>etag:</code> The ETag of the channel which is an HTTP header used for web browser cache validation.</p> <p>The historical data is stored in five directories named according to the type of data inside it. Each directory contains JSON files corresponding to the data described above. <strong>'historical_sample.zip'</strong> contains a small sample of the historical dataset.</p> <p>- <strong>Time-series Data (time_series_anon.zip)</strong></p> <p>This consists of time-series data (collected every 8 hours) of Twitter users and tweets collected from the blackmarket services between the period of March-June, 2019. We collect the following time-series data for retweets and followers on Twitter:</p> <p><code>user_timeline</code>: This is a JSON list of tweet objects in the user’s timeline, which consists of the tweets posted, retweeted and quoted by the user. The file created at each time interval contains the new tweets posted by the user during each time interval.</p> <p><code>user_followers</code>: This is a JSON file containing the user ids of all the followers of a user that were added or removed from the follower list during each time interval.</p> <p><code>user_followees</code>: This is a JSON file consisting of the user ids of all the users followed by a user, i.e., the followees of a user, that were added or removed from the followee list during each time interval.</p> <p><code>tweet_details</code>: This is a JSON object representing a given tweet, collected after every time interval.</p> <p><code>tweet_retweets</code>: This is a JSON list of tweet objects representing the most recent 100 retweets of a given tweet, collected after every time interval.</p> <p>The time-series data is stored in directories named according to the timestamp of the collection time. Each directory contains sub-directories corresponding to the data described above. <strong>'time_series_sample.zip'</strong> contains a small sample of the time series dataset.</p> <p><strong>Data Anonymization</strong></p> <p>The data is anonymized by removing all Personally Identifiable Information (PII) and generating pseud-IDs corresponding to the original IDs. A consistent mapping between the original and pseudo-IDs is maintained to maintain the integrity of the data.</p> <p> </p>
Critical Gaps in Medical Research Reporting by Online News Media: Dataset
<p>Dataset for <strong>Critical Gaps in Medical Research Reporting by Online News Media</strong></p>
Examining the mediating role of damage control and cooperation on the relationship between self-image and behavioral impulsivity in online social media among adolescents
Open the record for dataset details and reuse information.
Dataset Content Analysis Framing Inequality in Spanish Online Media
<p>Dataset Content Analysis Framing Inequality in Spanish Online Media. Project: News, networks and users in the<br> Hybrid Media System (Newsnet)</p>
Safer Online Lives: Use of the Internet & Social Media by People With Intellectual Disabilities
ClinicalTrials.gov study NCT04919213. IPD Sharing: NO. Countries: 1. Publications: 6.
Social Media Use ,Online Health Information Seeking and Knowledge on Hypertension
ClinicalTrials.gov study NCT04451876. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Figure 3 from: Skejo J, Connors M, Hendriksen M, Lambert N, Chong G, McMaster I, Monaghan N, Rentz D, Richter R, Rose K, Franjević D (2020) Online social media tells a story of Anaselina, Paraselina, and Selivinga (Orthoptera, Tetrigidae), rare Australian pygmy grasshoppers. ZooKeys 948: 107-119. https://doi.org/10.3897/zookeys.948.52910
Figure 3 The Angled Australian barkhopper, Paraselina brunneri (= P. multifora syn. nov.). A, B, D a female from Upper Orara (Nick Lambert) C a female from Lansdowne forest (Reiner Richter) E a male from Mt. Glorious (Griffin Chong) F individual from Mt. Mellum (Ian McMaster).
Figure 2 from: Skejo J, Connors M, Hendriksen M, Lambert N, Chong G, McMaster I, Monaghan N, Rentz D, Richter R, Rose K, Franjević D (2020) Online social media tells a story of Anaselina, Paraselina, and Selivinga (Orthoptera, Tetrigidae), rare Australian pygmy grasshoppers. ZooKeys 948: 107-119. https://doi.org/10.3897/zookeys.948.52910
Figure 2 The Tiny helmed groundhopper, Anaselina minor, a male in the natural habitat in Wooroonooran (Matthew Connors). Note the uncovered part of the abdomen and bilobate apex of the flat pronotum.
Figure 6 from: Skejo J, Connors M, Hendriksen M, Lambert N, Chong G, McMaster I, Monaghan N, Rentz D, Richter R, Rose K, Franjević D (2020) Online social media tells a story of Anaselina, Paraselina, and Selivinga (Orthoptera, Tetrigidae), rare Australian pygmy grasshoppers. ZooKeys 948: 107-119. https://doi.org/10.3897/zookeys.948.52910
Figure 6 Habitat of the Tribulation helmed groundhopper, Seliving tribulata. Rainforest in Kuranda, Queensland, rich in dry leaflitter (David Rentz).
Figure 5 from: Skejo J, Connors M, Hendriksen M, Lambert N, Chong G, McMaster I, Monaghan N, Rentz D, Richter R, Rose K, Franjević D (2020) Online social media tells a story of Anaselina, Paraselina, and Selivinga (Orthoptera, Tetrigidae), rare Australian pygmy grasshoppers. ZooKeys 948: 107-119. https://doi.org/10.3897/zookeys.948.52910
Figure 5 The Tribulation helmed groundhopper, Selivinga tribulata, Living specimens in natural habitat. A Female from Kuranda (David Rentz) B male from Kuranda (David Rentz), male from Tully Range (Matthew Connors) D nymph from Redlynch (Matthew Connors) E, G a male from Kingfisher park (Nick Monaghan) F female from Speewah (Matthew Connors).
Figure 4 from: Skejo J, Connors M, Hendriksen M, Lambert N, Chong G, McMaster I, Monaghan N, Rentz D, Richter R, Rose K, Franjević D (2020) Online social media tells a story of Anaselina, Paraselina, and Selivinga (Orthoptera, Tetrigidae), rare Australian pygmy grasshoppers. ZooKeys 948: 107-119. https://doi.org/10.3897/zookeys.948.52910
Figure 4 The Triple-bump Australian barkhopper, Paraselina trituberculata (Sjöstedt, 1932), a male in his habitat in Wilsons Creek (Kathy Rose).
Figure 1 from: Skejo J, Connors M, Hendriksen M, Lambert N, Chong G, McMaster I, Monaghan N, Rentz D, Richter R, Rose K, Franjević D (2020) Online social media tells a story of Anaselina, Paraselina, and Selivinga (Orthoptera, Tetrigidae), rare Australian pygmy grasshoppers. ZooKeys 948: 107-119. https://doi.org/10.3897/zookeys.948.52910
Figure 1 Updated distribution map of the Australian pygmy grasshoppers – Anaselina minor, Paraselina brunneri, P. trituberculata, and Selivinga tribulata. Each species is represented by its unique symbol, the silhouette of the species pronotum. The small map of Australia shows two regions inhabited by the barkhoppers.
Survei Pengguna Media Sosial dan Pemilik Toko Online dalam Promosi di Kolom Komentar Influencer
<p>Dataset survei pengguna media sosial dan pemilik toko online pada promosi melalui kolom komentar influencer</p>
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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)
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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.