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<p>Event databases play a crucial role in documenting and examining spatiotemporally distributed events ranging from protests over disasters, to public health emergencies. The Global Database of Events, Language, and Tone (GDELT) is notable for its comprehensive automated cataloging of events from many news sources. By extension, its extensive scope augments the risk of counting unique events repeatedly because they are reported by multiple sources. This leads to an inaccurate assessment of event level dynamics. This article presents a new, automated deduplication technique specifically designed to improve the event identification and counting accuracy of GDELT data. By consolidating redundant event entries into a unified, comprehensive record, our approach effectively mitigates the issue of overcounting, while enhancing the integrity and usefulness of the data. We assess the effectiveness of our method by conducting thorough algorithmic testing, and by comparing it to other established datasets such as Armed Conflict Location and Event Dataset (ACLED) and Integrated Crisis Early Warning System (ICEWS). The comparative analysis employs the deduplicated GDELT data to predict local protest levels, demonstrating that our deduplication procedure not only decreases the number of overcounted events but also better aligns GDELT with other event databases, thereby validating the effectiveness of our methodology. Our findings inform empirical research dependent on media-reported event databases like GDELT, with broader implications for other fields reliant on data collection and sources affected by directional measurement error.</p>

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Deduplication of the media-based event databases

  • 期刊论文

  • 发表日期：

    2025年7月15日

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  • Deepti Joshi,
  • Regina Werum,
  • Dalton Hazelwood,
  • Shawn Ratcliff,
  • Ashok Samal,
  • Leen-Kiat Soh

摘要

Event databases play a crucial role in documenting and examining spatiotemporally distributed events ranging from protests over disasters, to public health emergencies. The Global Database of Events, Language, and Tone (GDELT) is notable for its comprehensive automated cataloging of events from many news sources. By extension, its extensive scope augments the risk of counting unique events repeatedly because they are reported by multiple sources. This leads to an inaccurate assessment of event level dynamics. This article presents a new, automated deduplication technique specifically designed to improve the event identification and counting accuracy of GDELT data. By consolidating redundant event entries into a unified, comprehensive record, our approach effectively mitigates the issue of overcounting, while enhancing the integrity and usefulness of the data. We assess the effectiveness of our method by conducting thorough algorithmic testing, and by comparing it to other established datasets such as Armed Conflict Location and Event Dataset (ACLED) and Integrated Crisis Early Warning System (ICEWS). The comparative analysis employs the deduplicated GDELT data to predict local protest levels, demonstrating that our deduplication procedure not only decreases the number of overcounted events but also better aligns GDELT with other event databases, thereby validating the effectiveness of our methodology. Our findings inform empirical research dependent on media-reported event databases like GDELT, with broader implications for other fields reliant on data collection and sources affected by directional measurement error.