RIP Twitter API: A eulogy to its vast research contributions

• URL: http://arxiv.org/abs/2404.07340v1
• Date: Wed, 10 Apr 2024 20:39:24 GMT
• Title: RIP Twitter API: A eulogy to its vast research contributions
• Authors: Ryan Murtfeldt, Naomi Alterman, Ihsan Kahveci, Jevin D. West,
• Abstract summary: This study collects and tabulates the number of studies, number of citations, dates, major disciplines, and major topic areas that used Twitter data between 2006 and 2023. Since 2006, a total of 27,453 studies have been published in 7,432 publication venues, with 1,303,142 citations, across 14 disciplines. Major topics include: information dissemination, assessing the credibility of tweets, strategies for conducting data research, detecting and analyzing major events, and studying human behavior.
• Score: 1.1687067206676627
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Since 2006, Twitter's Application Programming Interface (API) has been a treasure trove of high-quality data for researchers studying everything from the spread of misinformation, to social psychology and emergency management. However, in the spring of 2023, Twitter (now called X) began changing $42,000/month for its Enterprise access level, an essential death knell for researcher use. Lacking sufficient funds to pay this monthly fee, academics are now scrambling to continue their research without this important data source. This study collects and tabulates the number of studies, number of citations, dates, major disciplines, and major topic areas of studies that used Twitter data between 2006 and 2023. While we cannot know for certain what will be lost now that Twitter data is cost prohibitive, we can illustrate its research value during the time it was available. A search of 8 databases and 3 related APIs found that since 2006, a total of 27,453 studies have been published in 7,432 publication venues, with 1,303,142 citations, across 14 disciplines. Major disciplines include: computational social science, engineering, data science, social media studies, public health, and medicine. Major topics include: information dissemination, assessing the credibility of tweets, strategies for conducting data research, detecting and analyzing major events, and studying human behavior. Twitter data studies have increased every year since 2006, but following Twitter's decision to begin charging for data in the spring of 2023, the number of studies published in 2023 decreased by 13% compared to 2022. We assume that much of the data used for studies published in 2023 were collected prior to Twitter's shutdown, and thus the number of new studies are likely to decline further in subsequent years.

Related papers

• Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021 [73.06361680847708]
  In 1960 14% of 333 research fields were related to AI, but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
  arXiv  Detail & Related papers  (2023-06-15T14:08:07Z)
• Just Another Day on Twitter: A Complete 24 Hours of Twitter Data [39.98744837726886]
  We have collected all 375 million tweets published within a 24-hour time period starting on September 21, 2022. This is the first complete 24-hour Twitter dataset that is available for the research community.
  arXiv  Detail & Related papers  (2023-01-26T21:28:40Z)
• Manipulating Twitter Through Deletions [64.33261764633504]
  Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. We find that a small fraction of accounts delete a large number of tweets daily. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms.
  arXiv  Detail & Related papers  (2022-03-25T20:07:08Z)
• Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions [0.0]
  The exoskeleton market is projected to increase by multiple times of its current value within the next two years. It is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction. This work presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022.
  arXiv  Detail & Related papers  (2021-11-04T19:36:01Z)
• Extracting Feelings of People Regarding COVID-19 by Social Network Mining [0.0]
  dataset of COVID-related tweets in English language is collected. More than two million tweets from March 23 to June 23 of 2020 are analyzed.
  arXiv  Detail & Related papers  (2021-10-12T16:45:33Z)
• CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Information [0.0]
  CML-COVID is a COVID-19 Twitter data set of 19,298,967 million tweets from 5,977,653 unique individuals. These tweets were collected between March 2020 and July 2020 using the query terms coronavirus, covid and mask related to COVID-19.
  arXiv  Detail & Related papers  (2021-01-28T18:59:10Z)
• Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions [1.6571886312953874]
  An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. This paper presents a large-scale study based on data mined from Twitter.
  arXiv  Detail & Related papers  (2020-05-18T15:53:13Z)
• Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline [47.434392695347924]
  RecSys 2020 Challenge organized by ACM RecSys in partnership with Twitter using this dataset. This paper touches on the key challenges faced by researchers and professionals striving to predict user engagements.
  arXiv  Detail & Related papers  (2020-04-28T23:54:33Z)
• A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow [69.5231754305538]
  Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining. Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
  arXiv  Detail & Related papers  (2020-04-18T08:19:46Z)
• #MeToo on Campus: Studying College Sexual Assault at Scale Using Data Reported on Social Media [71.74529365205053]
  We analyze the influence of the # trend on a pool of college followers. The results show that the majority of topics embedded in those # tweets detail sexual harassment stories. There exists a significant correlation between the prevalence of this trend and official reports on several major geographical regions.
  arXiv  Detail & Related papers  (2020-01-16T18:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.