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Titlebook: Social Informatics; 10th International C Steffen Staab,Olessia Koltsova,Dmitry I. Ignatov Conference proceedings 2018 Springer Nature Switz

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樓主: trace-mineral
21#
發(fā)表于 2025-3-25 04:54:56 | 只看該作者
Towards Understanding Communication Behavior Changes During Floods Using Cell Phone Dataupport-seeking behavior. Regressions on network features indicate that people in a large city in Senegal might have less access to resources outside of their neighborhoods and require better infrastructures.
22#
發(fā)表于 2025-3-25 08:50:01 | 只看該作者
Text-Based Detection and Understanding of Changes in Mental Health we employ propensity score matching to identify factors that correlate with an increase or a decrease in mental health forum contributions. Our work provides some of the first insights into detecting and understanding social media users’ changes in mental health states over time.
23#
發(fā)表于 2025-3-25 14:09:49 | 只看該作者
Evaluating the Search and Rescue Strategies of Post Disasterement. Besides, quantity priority is proved to be more efficient than normal random search, especially when the distribution of survivors is not even.. The findings of this paper provide important information for selecting effective analytical models in SAR management to save survivors.
24#
發(fā)表于 2025-3-25 19:25:21 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/s/image/869546.jpg
25#
發(fā)表于 2025-3-25 22:25:42 | 只看該作者
26#
發(fā)表于 2025-3-26 00:33:18 | 只看該作者
27#
發(fā)表于 2025-3-26 06:03:07 | 只看該作者
Partial and Overlapping Community Detection in Multiplex Social Networksue percolation algorithm is based, including cliques and clique adjacency, to handle the presence of multiple types of ties. We also provide an experimental characterization of the communities that our method can identify.
28#
發(fā)表于 2025-3-26 09:42:48 | 只看該作者
978-3-030-01158-1Springer Nature Switzerland AG 2018
29#
發(fā)表于 2025-3-26 13:50:05 | 只看該作者
30#
發(fā)表于 2025-3-26 17:05:16 | 只看該作者
Using Twitter Hashtags to Gauge Real-Time Changes in Public Opinion: An Examination of the 2016 US P as a data source for gauging real-time changes in public opinion. This paper examines trending Twitter hashtags following the release of James Comey’s letter to Congress shortly before the 2016 US presidential election day. I find that trending Twitter hashtags mirror and, more importantly, precede the poll results of the same period.
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