Published Time: 2026-06-07T00:00:00Z
这篇文章发布于2026年6月7日,这是一个未来的时间点,表明这是一篇预测性内容。这个时间点对于理解文章中的预测和趋势分析很重要,但需要读者意识到这是前瞻性内容而非已发生的事件。
Published Time: 2026-06-07T00:00:00Z
这篇文章发布于2026年6月7日,这是一个未来的时间点,表明这是一篇预测性内容。这个时间点对于理解文章中的预测和趋势分析很重要,但需要读者意识到这是前瞻性内容而非已发生的事件。
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[inaudible 00:26:06]
TYPO
Through spatio-temporalfiltering of messages we are able to observeevolution of topic signal that is consistent with rumor theory’s predictions. In the area immediately surrounding Moore wefind the strongest evidence that rumoring is an evolutionary process, as the topic and tone of messages shifts notably over thisthree-day span. Although these results may not necessarily generalize to all events, we dofind some initial support thatrumoring is a process marked by topical evolution over time. Once again, the spatio-temporalfiltering approach proves to berobust tool for measuring signal of hazard-related rumoring.
Summary findings.
Using what is known about social responses to disaster events(impending or realized), we selectivelyfilter timestamped streams of geolocated, informal communication activity by timeand location in order to identify surges of rumoring activity in response to a disaster. Spatio-temporalfiltering enhances ourability to detect events by utilizing the signal produced by sources that are known (or expected) to produce reliable infor-mation, thereby enhancing our ability to detect distinct activity patterns above and beyond typical global signal (i.e. back-ground noise representing the array of signals irrelevant to our focus)
Filtering technique relies on timestamp and geolocation.
Per Sloan (2015), only 0.85% of tweets are geotagged (approx. 4M tweets per day)
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142209