Selected article for: "depth study and large scale"

Author: Mujib, Munif Ishad; Heidenreich, Hunter Scott; Murphy, Colin J.; Santia, Giovanni C.; Zelenkauskaite, Asta; Williams, Jake Ryland
Title: NewsTweet: A Dataset of Social Media Embedding in Online Journalism
  • Cord-id: 0cfpq8d2
  • Document date: 2020_8_6
  • ID: 0cfpq8d2
    Snippet: The inclusion of social media posts---tweets, in particular---in digital news stories, both as commentary and increasingly as news sources, has become commonplace in recent years. In order to study this phenomenon with sufficient depth, robust large-scale data collection from both news publishers and social media platforms is necessary. This work describes the construction of such a data pipeline. In the data collected from Google News, 13% of all stories were found to include embedded tweets, w
    Document: The inclusion of social media posts---tweets, in particular---in digital news stories, both as commentary and increasingly as news sources, has become commonplace in recent years. In order to study this phenomenon with sufficient depth, robust large-scale data collection from both news publishers and social media platforms is necessary. This work describes the construction of such a data pipeline. In the data collected from Google News, 13% of all stories were found to include embedded tweets, with sports and entertainment news containing the largest volumes of them. Public figures and celebrities are found to dominate these stories; however, relatively unknown users have also been found to achieve newsworthiness. The collected data set, NewsTweet, and the associated pipeline for acquisition stand to engender a wave of new inquiries into social content embedding from multiple research communities.

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