当你是一个对政治感兴趣的推特用户时,谁适合被关注?

Lorena Recalde, Aigul Kaskina
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引用次数: 6

摘要

民主社会中公民参与和政治参与的程度可能因人而异。同样,用户政治参与的多样性也可以在在线平台上观察到。例如,根据Twitter的活跃程度,Twitter用户可能被划分为政治活跃度高或政治活跃度低的公民。确定用户对政治的兴趣程度将有助于为他们提供有意义的建议,例如要关注的政治人物、谈论政治的推文和面向政治的列表等。然而,由于信息过载和Twitter上发布的话题范围广泛,产生个性化的政治相关建议成为一个问题。在本文中,我们通过提出一项初步工作来解决这一挑战,其中我们i)确定Twitter用户样本的政治兴趣程度(DoIP), ii)测量该程度与Twitter好友的DoIP的相关性,旨在将其用于后续建议。在设计对政治感兴趣的用户的关注推荐系统时,i)和ii)都可以作为核心基础。鉴于目前缺乏对Twitter用户的后续建议的研究,这种研究方法是新颖的。厄瓜多尔政治背景下的真实数据实验表明,我们的方法在识别Twitter公民的DoIP及其与朋友的积极联系方面是有效的。
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Who is suitable to be followed back when you are a Twitter interested in Politics?
The degree of citizens participation and political involvement within democratic societies may vary from one person to another. Similarly, the diversity of political engagement of users might be observed in online platforms. For example, Twitter users might be characterized as highly politically active or poorly politically active citizens, according to their Twitter activity. Identifying the level of interest in politics of a user would be relevant to provide them with meaningful recommendations such as political actors to follow, tweets talking about politics, and political-oriented lists, among others. However, due to the information overload and the wide range of topics posted on Twitter, generating personalized political-related suggestions becomes a problem. In this paper, we address this challenge by presenting a preliminary work where we i) identify the degree of interest in politics (DoIP) of a sample of Twitter users and ii) measure the correlation of this degree with their Twitter friends' DoIP, aiming to use it in following back recommendations. Both i) and ii) can be considered as core bases when designing a following back recommender system for users interested in politics. This research approach is novel with respect to the state of the art given the current lack of studies in following back recommendations for Twitter users. Experiments on real data in the context of politics in Ecuador show the effectiveness of our approach in identifying the DoIP of Twitter citizens and the positive association of it with their friends'.
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