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10 results for “retweets”
Japanese "Abe" Tweets from 2019-02-10 to 2020-10-07 (15,407,811 tweets and 114,231,250 retweets)
<p><strong>Abstract</strong> (our paper)</p> <p>To examine conservative–liberal differences in the extent to which partisan tweets reach less partisan moderate users in a nonwestern context, we analyzed a network of retweets about former Japanese Prime Minister Shinzo Abe. The analyses consistently demonstrated that partisan tweets originating from the conservative cluster reach a wider range of moderate users than those from the liberal cluster. Network analyses revealed that while the conservative and the liberal clusters’ internal structures were similar, the conservative cluster reciprocated the follows from moderate accounts at a higher rate than the liberal cluster. In addition, moderate accounts reciprocated the conservative cluster’s following at a higher rate than they did for the liberal cluster. The analysis of tweet content showed no difference in the frequency of hashtag use between conservatives and liberals, but there were differences in the use of emotion words and linguistic expressions. In particular, emotion words related to the propagation of messages, such as those expressing “dislike”, were used more frequently by conservatives, while the use of adjectives by conservatives was closer to that of moderate users, indicating that conservative tweets are more palatable for moderate users than liberal tweets.</p> <p><strong>Data</strong></p> <p>Abe.tsv.gz:<br> The first column is the tweet id, the second column is the tweet id of the retweet source, and the third column is the date and time (JST) when the tweet was posted.<br> This data was collected by giving the query "安倍 OR アベ" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets. The second column is empty if the tweet is not a retweet.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Mitsuo Yoshida, Takeshi Sakaki, Tetsuro Kobayashi, Fujio Toriumi. Japanese conservative messages propagate to moderate users better than their liberal counterparts on Twitter. <em>Scientific Reports</em>. vol.11, article no.19224, 2021.<br> <a href="https://doi.org/10.1038/s41598-021-98349-2">https://doi.org/10.1038/s41598-021-98349-2</a></p>
replies and retweets with comments (quotes) of the tweets 1261179829895995394 and 1263036802295902208
<p>The dataset contains (recursive) replies and retweets with comments, as well as their (recursive) replies of two "initial" tweets: 1261179829895995394 and 1263036802295902208.</p> <p>The code that was used is published here: <a href="https://github.com/millawell/reretweets">https://github.com/millawell/reretweets</a></p> <p>The script takes the following steps to obtain tweets and quotes:</p> <ol> <li>It downloads all direct replies (recursively) to the tweet</li> <li>It searches for the tweet id, to get quotes</li> <li>For each tweet, that quoted the original tweet, it again retrieves all replies.</li> </ol>
Italian Covid-19 Retweet Network (2020-2022)
<p><strong>Description</strong></p> <p>This repository contains directed retweet interactions between anonymized Twitter (now X) users.</p> <p>Data is stored in five csv files, each one relating to a different phase of the pandemic in Italy.</p> <ul> <li>early covid (01/01/2020 – 08/03/2020): from Covid’s first tracing in Wuhan,<br>China, up to the first lockdown in Italy;</li> <li>pre vaccine (09/03/2020 – 31/10/2020): from the first Italian lockdown to the start of the vaccination campaign;</li> <li>early vaccine (01/11/2020 – 16/04/2021): the first months of the vaccination campaign;</li> <li>vaccine drive (17/04/2021 - 31/07/2021): the main phase of intensive vaccination;</li> <li>late vaccine (01/08/2021 – 31/12/2021): phase in which a significant portion<br>of the Italian population was fully vaccinated;</li> </ul> <p>CSV files are structured as follows: </p> <p><em>from,to,weight</em></p> <p> </p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong><br>This work is supported by (i) the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, ”SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu); (ii) SoBigData.it which receives funding from the European Union –<br>NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: ”SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021; (iii) EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).</p>
Japanese COVID-19 Tweets from 2020-01-17 to 2020-04-30 (40,720,545 tweets and 105,317,606 retweets)
<p><strong>Abstract</strong> (our paper)</p> <p>The spread of COVID-19, the so-called new coronavirus, is currently having an enormous social and economic impact on the entire world. Under such a circumstance, the spread of information about the new coronavirus on SNS is having a significant impact on economic losses and social decision-making. In this study, we investigated how the new type of coronavirus has become a social topic in Japan, and how it has been discussed. In order to determine what kind of impact it had on people, we collected and analyzed Japanese tweets containing words related to the new corona on Twitter. First, we analyzed the bias of users who tweeted. As a result, it is clear that the bias of users who tweeted about the new coronavirus almost disappeared after February 28, 2020, when the new coronavirus landed in Japan and a state of emergency was declared in Hokkaido, and the new corona became a popular topic. Second, we analyzed the emotional words included in tweets to analyze how people feel about the new coronavirus. The results show that the occurrence of a particular social event can change the emotions expressed on social media.</p> <p><strong>Data</strong></p> <p>Tweets_YYYY-MM-DD.tsv.gz:<br> The first column is the tweet id, the second column is the date and time (JST) when the tweet was posted, the third column is the flag as to whether the tweet was used for emotion analysis or not, and the fourth column is the tweet id of the retweet source.<br> This data was collected by giving the query "新型肺炎 OR 武漢 OR コロナ OR ウイルス OR ウィルス" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets.<br> We conducted emotion analysis on tweets, excluding retweets and tweets containing links. The fourth column is empty if the tweet is not a retweet.</p> <p>KL-Divergence.tsv.gz:<br> The first column is the date (JST), and the second column is the value of KL-Divergence that calculated the bias of the users who posted tweets related to COVID-19.<br> The value of KL-Divergence was calculated with all users appearing in Tweets_YYYY-MM-DD.tsv.gz. Based on the sampling stream data, we determined that if the value is below 0.6, there is no bias.</p> <p>Emotions_by_ML-Ask.tsv.gz:<br> The first column is the date (JST), the second and subsequent columns are the number of tweets for each emotion, and the last column is the number of tweets analyzed for the day.<br> For this analysis, we only used tweets with a value of 1 in the third column of Tweets_YYYY-MM-DD.tsv.gz. We used <a href="https://github.com/ikegami-yukino/pymlask">pymlask</a> (Python implementation of <a href="http://doi.org/10.5334/jors.149">ML-Ask</a>) to estimate the emotion of the tweet.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Fujio Toriumi, Takeshi Sakaki, Mitsuo Yoshida. Social Emotions Under the Spread of COVID-19 Using Social Media. <em>Transactions of the Japanese Society for Artificial Intelligence (in Japanese)</em>. vol.35, no.4, pp.F-K45_1-7, 2020.<br> 鳥海不二夫, 榊剛史, 吉田光男. ソーシャルメディアを用いた新型コロナ禍における感情変化の分析. <em>人工知能学会論文誌</em>. vol.35, no.4, pp.F-K45_1-7, 2020.<br> <a href="https://doi.org/10.1527/tjsai.F-K45">https://doi.org/10.1527/tjsai.F-K45</a></p>
Japanese Sample Tweets, COVID-19 Keywords and Emotions from 2020-01-01 to 2020-06-30 (88,495,817 tweets and 47,539,139 retweets)
<p><strong>Data</strong></p> <p>Tweets_YYYY-MM.tsv.gz:<br> The first column is the tweet id, the second column is the date and time (JST) when the tweet was posted, the third column is the tweet id of the mention destination, the fourth column is the tweet id of the retweet source, the fifth column is the place id, the sixth column is the country code, the seventh column is the prefecture code if the country code is JP, and the eighth column is the COVID-19-related keyword included in the tweet. Columns with no information are empty. For example, a tweet with an empty eighth column is not a COVID-19-related tweet.<br> This data was collected using statuses/sample of the Twitter Streaming API, narrowed down by language=ja. Therefore, most of the tweets are Japanese tweets. Also, due to a failure of the data collection server, a large number of tweets on January 22 are missing :(<br> We have used 肺炎, コロナ and COVID (case insensitive) as keywords related to COVID-19.</p> <p>Emotions_YYYY-MM.tsv.gz:<br> The first column is the tweet id, the second and subsequent columns are the number of occurrences of each emotional keyword. Column names (types of emotion) are shown in the first row.<br> We used <a href="http://arakilab.media.eng.hokudai.ac.jp/~ptaszynski/repository/mlask.htm">mlask43-simple</a> (Perl implementation of <a href="http://doi.org/10.5334/jors.149">ML-Ask</a>) with dictionaries used in <a href="https://github.com/ikegami-yukino/pymlask">pymlask</a> to extract emotional keywords from the tweet.</p> <p><strong>Publication</strong></p> <p>This data set was created for my study. If you make use of this data set, please cite:<br> Mitsuo Yoshida. The State of Social Media During the COVID-19 Pandemic: Japan's Situation, Research Trends and Public Datasets. <em>Journal of Japanese Society for Artificial Intelligence (in Japanese)</em>. vol.35, no.5, pp.644-653, 2020.<br> 吉田光男. <a href="http://id.nii.ac.jp/1004/00010708/">COVID-19流行下におけるソーシャルメディア ―日本での状況と研究動向・公開データセット―</a>. <em>人工知能</em>. vol.35, no.5, pp.644-653, 2020.</p>
Italian Retweets Timeseries
<p>This dataset contains temporal data of about 5,121,132 retweets from 47,947 users took from the italian twittersphere published between 18/06/2018 and 01/07/2018. </p> <p>Refer to the paper below for more details.</p> <p>Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., & Tesconi, M. (2019). RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter. <em>arXiv preprint arXiv:1902.04506</em>.</p>
Figure 1. Common-retweeter networks of 65 hub accounts of #southchinasea during 4 Nov 2016 - 7 Jun 2018
<p>The South China Sea dispute on Twitter: public diplomacy between China and the United States<br> Figure 1.</p>
Figure 2. Mutual (excluding self) retweeting of 57 hub accounts in #southchinasea during 4 Nov 2016 - 7 Jun 2018
<p>The South China Sea dispute on Twitter: public diplomacy between China and the United States<br> Figure 2.</p>
Figure. 1 Common-retweeter networks of 65 hub accounts of #southchinasea during 4 Nov 2016 – 7 Jun 2018
<p>Figure 1 in The South China Sea dispute on Twitter: public diplomacy between China and the United States - Media International Australia</p>
A one percent sample of German Twitter retweet traffic
<p>This dataset includes a one percent sample of German-language Twitter retweets in Twitter raw data format. For each day, all retweets are stored in json data format (one entry per line).</p> <p>The dataset was recorded using Tweepy and exported from a MongoDB database. It is intended to be imported into a MongoDB database to run analytical queries. It is not intended to be processed as is.</p> <p>The dataset covers 60 consecutive days and ends on 01/25/2023.</p> <p>The dataset was recorded as part of this study.</p> <p> </p> <p>Kratzke, N. How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers. <em>Computers</em> <strong>2023</strong>, <em>12</em>, 57. https://doi.org/10.3390/computers12030057</p>
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