Behavioral differences: insights, explanations and comparisons of French and US Twitter usage during elections
Behavioral differences: insights, explanations and comparisons of French and US Twitter usage during elections
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行为差异：选举期间法国和美国 Twitter 使用情况的见解、解释和比较
DOI：
10.1007/s13278-019-0611-9
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发表时间：
2020
影响因子：
2.8
通讯作者：
Ian Davidson, Antoine Gourru
中科院分区：
文献类型：
--
作者：
Ian Davidson, Antoine Gourru
社交网络和社交媒体在观察和影响政治格局的形成和动态变化方面发挥了关键作用。在全国选举这样的事件中尤其如此，Facebook(Williams和Gulati，见：美国政治科学协会年会论文集，2009)和Twitter(Larsson和Moe，New Med Soc 14(5)：729-747,2012)的早期研究表明了这一点。不出所料，为了更好地理解和简化这些网络，使用了社区发现方法，例如卢瓦恩方法(Blondel et al.参见《统计力学理论》Exp 2008(10)：P10008,2008)以理解选举(Gaumont等人)。载于《公共科学图书馆》13(9)：e0201879,2018年)。然而，大多数基于社区的研究首先根据(例如)关注者、转发或友谊属性将复杂的Twitter数据简化为一个单一的网络。这需要忽略一些信息或将许多类型的信息组合到一个图表中，这可以掩盖许多洞察力。在本文中，我们将Twitter数据作为一个带时间戳的顶点标记图来研究。图的结构可以由用户之间的结构关系给出，如转发、友谊或追随者关系，而个人的行为则由他们的发帖行为给出，这被建模为一个随时间演化的顶点标签。我们探索利用现有的社区发现方法，仅使用结构数据来发现社区，然后使用行为数据来描述这些社区。我们探索了两个免费的方向：(1)基于社区使用情况创建标签分类和(2)有效地描述社区，扩展了我们最近发表的工作。我们已经创建了两个数据集，分别用于法国和美国的选举，我们从这些数据集中比较和对比对标签使用的见解。
Social networks and social media have played a key role for observing and influencing how the political landscape takes shape and dynamically shifts. It is especially true in events such as national elections as indicated by earlier studies with Facebook (Williams and Gulati, in: Proceedings of the annual meeting of the American Political Science Association, 2009) and Twitter (Larsson and Moe in New Med Soc 14(5):729–747, 2012). Not surprisingly in an attempt to better understand and simplify these networks, community discovery methods have been used, such as the Louvain method (Blondel et al. in J Stat Mechanics Theory Exp 2008(10):P10008, 2008) to understand elections (Gaumont et al. in PLoS ONE 13(9):e0201879, 2018). However, most community-based studies first simplify the complex Twitter data into a single network based on (for example) follower, retweet or friendship properties. This requires ignoring some information or combining many types of information into a graph, which can mask many insights. In this paper, we explore Twitter data as a time-stamped vertex-labeled graph. The graph structure can be given by astructuralrelation between the users such as retweet, friendship or follower relation, whilst thebehaviorof the individual is given by their posting behavior which is modeled as a time-evolving vertex labels. We explore leveraging existing community discovery methods to find communities using just the structural data and then describe these communities using behavioral data. We explore two complimentary directions: (1) creating a taxonomy of hashtags based on their community usage and (2) efficiently describing the communities expanding our recently published work. We have created two datasets, one each for the French and US elections from which we compare and contrast insights on the usage of hashtags.
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DOI：
10.1109/tkde.2016.2553667
发表时间：
2016-08-01
影响因子：
8.9
作者：
Wong, Felix Ming Fai;Tan, Chee Wei;Chiang, Mung
通讯作者：
Chiang, Mung
社交网络中的观点发现和理解
DOI：
10.1145/3201064.3201076
发表时间：
2018
期刊：
Proceedings of the 10th ACM Conference on Web Science
影响因子：
--
作者：
M. Quraishi;P. Fafalios;E. Herder
通讯作者：
E. Herder
新媒体、竞选活动和 2008 年 Facebook 选举
影响因子：
2.4
作者：
Thomas J. Johnson;D. Perlmutter
通讯作者：
D. Perlmutter
影响因子：
3.6
作者：
Bryden, John;Funk, Sebastian;Jansen, Vincent A. A.
通讯作者：
Jansen, Vincent A. A.
影响因子：
4.9
作者：
Aragon, Pablo;Kappler, Karolin Eva;Volkovich, Yana
通讯作者：
Volkovich, Yana