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2019-10-09
This case study is part of a series on the ethics of social media data use by third parties.
Résultats de recherche
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2020-02-06
<b>Data collection</b>: Using Twitter's Search API, we collected 363,706 public tweets (in English) mentioning #elxn43 and directed at 1,116 candidates running for office during the 2019 Federal election in Canada. Tweets were collected between September 29 and October 28, 2019.
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<b>Manual coding</b>: The data set contains a random sample of 3,637 tweets (1% sample) hand coded as either 'toxic' or 'insulting' by using three coders. Only tweets that were flagged by all three coders were considered as either 'toxic' (TOXICITY_3CODERS_AGREE=1) or 'insulting' (INSULT_3CODERS_AGREE = 1).
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2020-11-15
Social Media Privacy in Canada - 2020
Borealis
Gruzd, Anatoliy;
Mai, Philip
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2020-05-10
To examine the “digital hygiene” practices of Canadians, we asked 1,500 online Canadian adults about where they get news about COVID-19 from, how often they encounter misinformation on this topic, and what do they do about it. The survey was conducted between April 9–17, 2020.
This report was produced by the Social Media Lab at Ted Rogers School of Management, Ryerson University. It is released as part of the Social Media Data Stewardship Project funded by the Canada Research Chairs Program, and the COVID-19 Misinformation Portal, a rapid response project funded by the Canadian Institutes of Health Research.
Borealis
Kargar, Katayoun;
Joksimovic, Darko
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2024-06-27
This dataset aims to investigate the dynamics of sewer blockages caused by wipes, including their probability of snagging and accumulation under different flow rates and sewer imperfection scenarios, as well as their dissipation rates in high-risk conditions. The scope includes raw data on snagging and accumulation and detailed measurements of cumulative dissipation and dissipation rates under varying conditions.
Borealis
Gruzd, Anatoliy;
Conroy, Nadia
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2018-04-05
<p>This is a data set of 482,251 public tweets and retweets (Twitter IDs) posted by the <a href="http://edchat.pbworks.com/w/page/219908/FrontPage">#edchat online community of educators</a> who discuss current trends in teaching with technology.</p>
<p>The data set was collected via Twitter's Streaming API between Feb 1, 2018 and Apr 4, 2018, and was used as part of the research on developing a <a href="https://dashboard.socialmediadata.org/educhat/">learning analytics dashboard for teaching and learning with Twitter</a>.</p>
<p>Following Twitter's terms of service, the data set only includes unique identifiers of relevant tweets. To collect the actual tweets that are part of this data set, you will need to use one of the available third party tools such as <a href="https://github.com/docnow/hydrator#readme">Hydrator </a>or <a href="https://github.com/DocNow/twarc">Twarc </a>("hydrate" function).</p>
<p>As part of this release, we are also attaching an enriched version of this data set that contains sentiment and opinion analysis labels that were produced by analyzing each tweet with the help of the <a href="http://www.nltk.org/api/nltk.sentiment.html">NLTK SentimentAnalyzer</a> Python package.</p>
<p>*This work was supported in part by <a href="https://www.ecampusontario.ca">eCampusOntario </a>and <a href="http://www.sshrc-crsh.gc.ca/">The Social Sciences and Humanities Research Council of Canada</a>.</p>
Borealis
Gruzd, Anatoliy
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2021-01-07
On October 2nd, 2020, U.S. President Donald Trump tweeted that he and the First Lady of the United States (FLOTUS), Melania Trump, both tested positive for COVID-19 (https://twitter.com/realDonaldTrump/status/1311892190680014849). Within seconds, his tweet received thousands of replies.
The dataset consists of 298,172 replies to Donald Trump’s tweet announcing his COVID-19 diagnosis, posted on October 2nd between 6am and 12:30pm (ET). It contains tweet ids, the toxicity scores (as calculated by Google's Perspective API via https://Communalytic.com) and tweet availability status values (via twarc library). Following Twitter’s API policy, we stripped metadata associated with each tweet. So, if you’d like to examine potential relationships between other metadata elements, you would need to recollect original tweets using tools like DocNow’s Hydrator first. The only downside of this approach is that tweets that have been blocked or deleted will not be recollected.
To help you get started, we also shared our Exploratory Data Analysis (EDA) Python script at https://github.com/RUSocialMediaLab/toxicityanalysis
Borealis
Soares, Felipe Bonow;
Saiphoo, Alyssa;
Gruzd, Anatoliy;
Mai, Philip
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2022-08-03
As part of our ongoing research on misinformation and disinformation of various types, we conducted an exploratory analysis of tweets discussing an unverified report that Russian forces engaged in a chemical attack in Mariupol, Ukraine. This claim was made on April 11 by Ukraine’s Azov regiment. At the time when this claim was first reported, Mariupol was surrounded by Russian troops, making it difficult, if not nearly impossible, for journalists to gain access to the city and to interview local sources. We were interested in examining how this claim was discussed on social media because if it was true, it had the potential to galvanize the world’s sentiments in support of Ukraine and against Russia.
Using Twitter’s Academic Track API, we retroactively collected 246,189 public tweets posted between April 6 and 13, 2022 to analyze how Twitter users were discussing this claim. We collected tweets related to this case a few days before and after April 11 to capture speculation before the accusation, and the reaction to it. We used the search query “chemical (weapons OR weapon) (Mariupol OR Ukraine)” to collect data. (For data completeness, we kept 12,193 tweets referenced by one of the tweets in the search results.)
Borealis
Gruzd, Anatoliy;
Mai, Philip
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2021-05-20
The dataset contains 99,039 Tweet IDs of Twitter posts with #FilmYourHospital. It was collected using Netlytic.org between March 28 and April 9, 2020, by querying Twitter Search API (ver.1) very 15 minutes.
NOTES: 1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset. 2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc).
For more info about this dataset, read the following paper: https://doi.org/10.1177/2053951720938405
Borealis
Gruzd, Anatoliy;
Jacobson, Jenna;
Mai, Philip;
Dubois, Elizabeth
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2018-06-04
This is the second report in the series based on an online survey of 1,500 Canadians. Building on the first report that provides a snapshot of the social media usage trends in Canada, this second report analyzes social media users’ privacy perceptions and expectations.
Borealis
Kwon, K. Hazel;
Gruzd, Anatoliy
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2017-12-11
The dictionary was developed based on the two primary sources: (a) public lists of English swear words shared freely on websites such as www.noswearing.com, and (b) a custom-built dictionary of swear words and abbreviations (e.g., smfh, stfu, wtf, wth) derived from the manual reviews of over 60,000 Twitter messages by the research team.
The inter-coder reliability of the Twitter-derived swear words achieved 92.04% agreement, with kappa alpha = .87.
Borealis
Gruzd, Anatoliy;
Jacobson, Jenna;
Mai, Philip;
Dubois, Elizabeth
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2018-02-22
This report provides a snapshot of the social media usage trends among online Canadians adults in 2017.
Borealis
Dubois, Elizabeth;
Gruzd, Anatoliy;
Mai, Philip;
Jacobson, Jenna
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2018-12-06
The report examines the ways online Canadian adults are engaging politically on social media. This is the third and final report based on a census-balanced survey of 1,500 Canadians using quota sampling by age, gender, and geographical region. The other two reports in this series are: "The State of Social Media in Canada 2017" and "Social Media Privacy in Canada".
The series is published by the Social Media Lab, an interdisciplinary research lab at Ted Rogers School of Management, Ryerson University. The lab studies how social media is changing the ways in which people communicate, share information, conduct business and how these changes are impacting our society.
Borealis
May Yan
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2015-11-16
Citation Analysis of CMN432 group assignment bibliographies. Primary focus of this study is to investigate the types of information students used, if they could have used Summon to find all resources. How much of the resources were available in a speciality database - Compendex?
Borealis
Gruzd, Anatoliy;
Mai, Philip
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2020-07-12
This report provides a snapshot of the social media usage trends among online Canadian adults in 2020.
Borealis
Gruzd, Anatoliy;
Mai, Philip
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2020-08-19
The current dataset contains 237M Tweet IDs for Twitter posts that mentioned "COVID" as a keyword or as part of a hashtag (e.g., COVID-19, COVID19) between March and July of 2020.
Sampling Method: hourly requests sent to Twitter Search API using Social Feed Manager, an open source software that harvests social media data and related content from Twitter and other platforms.
NOTE:
1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset.
2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc).
3) This dataset, like most datasets collected via the Twitter Search API, is a sample of the available tweets on this topic and is not meant to be comprehensive. Some COVID-related tweets might not be included in the dataset either because the tweets were collected using a standardized but intermittent (hourly) sampling protocol or because tweets used hashtags/keywords other than COVID (e.g., Coronavirus or #nCoV).
4) To broaden this sample, consider comparing/merging this dataset with other COVID-19 related public datasets such as:
https://github.com/thepanacealab/covid19_twitter
https://ieee-dataport.org/open-access/corona-virus-covid-19-tweets-dataset
https://github.com/echen102/COVID-19-TweetIDs
Borealis
McGregor, Michael;
Moore, Aaron;
Stephenson, Laura
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2020-08-19
This submission consists of the 2014 TES dataset (Stata and SPSS versions), codebook, and methodology report.
Borealis
Ryerson University Social Media Lab;
The International Federation of Medical Students’ Associations
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2020-09-16
The COVID-19 Fact Checkers Dataset is a comprehensive list of over 200 active fact-checking organizations and groups that verify COVID-19 misinformation. The dataset is maintained by the Ryerson University’s Social Media Lab as part of an international initiative to study the proliferation of COVID-19 misinformation and to map fact-checking activities around the world in partnership with the World Health Organization (WHO). It was created to provide the public with a better understanding of the COVID-19 fact-checking ecosystem and is intended for use by policy makers and others to make data-informed decisions in the fight against COVID-19 misinformation.
Borealis
Gruzd, Anatoliy;
Mai, Philip
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2020-09-24
The dataset contains COVID-related claims reviewed by fact-checking organizations from around the world.
The dataset was collected between January 22, 2020 and January 31, 2020 using a custom Python script by querying Google Fact Check Tools API on a daily basis using search keywords: "Coronavirus" or "COVID".
Notes:
1. Only claims indexed by the Google Fact Check Tools are included. See more information about this resource at https://toolbox.google.com/factcheck/about
2. Google Fact Check Tools aggregates data from over 100 fact-checking organizations and each of them uses slightly different textual ratings, titles, and annotations. As a result, the dataset may contain links to multiple reviews of the same claim, reviewed by different fact-checkers.
3. Since the dataset contains claims in different languages, we used Google's Translation API to translate some fields of non-English claims (fields: "text_en", "claimant_en" and "textualRating_en"). We kept the original values as well.
Borealis
Gruzd, Anatoliy;
Mai, Philip;
Soares, Felipe Bonow;
Saiphoo, Alyssa
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2022-07-12
This report examines the extent to which Canadians are exposed to and might be influenced by pro-Kremlin propaganda on social media based on a census-balanced national survey of 1,500 Canadians conducted between May 12–31, 2022. Among other questions, the survey asked participants about their social media use, news consumption about the war in Ukraine, political leanings, as well as their exposure to and belief in common pro-Kremlin narratives.
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