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Titlebook: Influence and Behavior Analysis in Social Networks and Social Media; Mehmet Kaya,Reda Alhajj Book 2019 The Editor(s) (if applicable) and T

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書目名稱Influence and Behavior Analysis in Social Networks and Social Media
編輯Mehmet Kaya,Reda Alhajj
視頻videohttp://file.papertrans.cn/465/464762/464762.mp4
概述Includes behavior analysis data for predicting active and inactive users in Wikipedia.Contains book reading behavior data on Goodreads and how it can predict ‘Amazon Best Sellers‘.Ties into discussion
叢書名稱Lecture Notes in Social Networks
圖書封面Titlebook: Influence and Behavior Analysis in Social Networks and Social Media;  Mehmet Kaya,Reda Alhajj Book 2019 The Editor(s) (if applicable) and T
描述This timely book focuses on influence and behavior analysis?in the broader context of social network applications and social media.? Twitter accounts of telecommunications companies are analyzed.? Rumor sources in finite graphs with boundary effects by message-passing algorithms are identified..The coherent, state-of-the-art collection of chapters was initially selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM ‘17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. Original chapters coming from outside of the meeting round out the coverage. The result will appeal to researchers and students working in social network and social media analysis..
出版日期Book 2019
關(guān)鍵詞Social Network Analysis and Mining; Influence Analysis; Recommendation Systems; twitter analysis shaw c
版次1
doihttps://doi.org/10.1007/978-3-030-02592-2
isbn_ebook978-3-030-02592-2Series ISSN 2190-5428 Series E-ISSN 2190-5436
issn_series 2190-5428
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Temporal Model of the Online Customer Review Helpfulness Prediction with Regression Methods, prediction of the helpfulness rate of customer reviews to find the helpful reviews which are traditionally determined by the helpful voting results. In our study, we find that the helpfulness voting result of an online review is not constant over time. Therefore, predicting the voting result based
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Traits of Leaders in Movement Initiation: Classification and Identification,heir group? Do they initiate travel in new directions or are they just the first ones to start moving? Which attempts to initiate movement translate to leadership? In this paper we present a computational method to characterize and classify the types of leaders in movement initiation. We adapt a lea
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Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube,ated to 24 systematically chosen real-world events. For each of the 5.6 million messages, we first extracted emotion scores based on the eight basic emotions according to Plutchik’s wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the messaging behav
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Diffusion Algorithms in Multimedia Social Networks: A Novel Model,om marketing to user profiling and recommendation. Anyway, although OSNs are naturally formed by heterogeneous data, in the recent past, only a few works have considered an explicit use of multimedia in their models. In this paper, we explicitly take into account the intrinsic characteristics of mul
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