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Titlebook: Social Informatics; 9th International Co Giovanni Luca Ciampaglia,Afra Mashhadi,Taha Yasser Conference proceedings 2017 Springer Internatio

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11#
發(fā)表于 2025-3-23 10:38:33 | 只看該作者
12#
發(fā)表于 2025-3-23 17:29:59 | 只看該作者
13#
發(fā)表于 2025-3-23 20:09:34 | 只看該作者
Stance Classification in Out-of-Domain Rumours: A Case Study Around Mental Health Disorders Classification of stance in individual posts can then be useful to determine the veracity of a rumour. Research in this direction has looked at rumours in different domains, such as politics, natural disasters or terrorist attacks. However, work has been limited to in-domain experiments, i.e. train
14#
發(fā)表于 2025-3-24 01:14:10 | 只看該作者
Predicting Multiple Risky Behaviors via Multimedia Content abuse costs our society more than 484 billion dollars a year”, which is about thrice of what we spend on cancer. Researchers have started studying risky behaviors through big data from social media. However, to our best knowledge, most of the existing schemes focus on only one risky behavior, despi
15#
發(fā)表于 2025-3-24 03:51:37 | 只看該作者
16#
發(fā)表于 2025-3-24 07:37:46 | 只看該作者
Discovering the Typing Behaviour of Parkinson’s Patients Using Topic Modelsement. In this paper, we focus on detecting typing behaviour that is characteristic of patients suffering from Parkinson’s disease. We consider typing data obtained from subjects with and without Parkinson’s, and we present a framework based on topic models that determines the differing behaviours b
17#
發(fā)表于 2025-3-24 11:25:51 | 只看該作者
From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensemblf social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational dat
18#
發(fā)表于 2025-3-24 17:15:38 | 只看該作者
19#
發(fā)表于 2025-3-24 23:00:04 | 只看該作者
On Early-Stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learnersperform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the ea
20#
發(fā)表于 2025-3-25 01:01:37 | 只看該作者
Convergence of Media Attention Across 129 Countriesell-established political, cultural, and economic consequences, the degree to which media coverage and content is converging across countries has implications for international relations. To study this convergence, we use the daily data of the 100 topics that were over-reported in each country, comp
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