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一个 “@用户名” 里有什么?用户名的价值如何影响对微博作者的评判

What’s in a @name? How Name Value Biases Judgment of Microblog Authors
课程网址: https://videolectures.net/videos/icwsm2011_counts_microblog  
主讲教师: Scott Counts
开课单位: 信息不详。欢迎您在右侧留言补充。
开课时间: 2011-08-18
课程语种: 英语
中文简介:
偏见可以被定义为,当人们在面对从多个选项中做出决策的任务时所表现出的选择性偏袒。在线社区为其用户提供了大量的决策机会。用户在他们的关注行为、投票、评分以及其他决策任务中都会表现出偏见。我们研究了微博用户中因作者用户名价值而产生的偏见。我们描述了用户名价值偏见与粉丝数量之间的关系,并分别根据作者所受到的偏见模式以及读者所表现出的偏见模式对作者和读者进行了聚类。对于作者,我们发现来自知名用户名(例如,@美国有线电视新闻网(CNN))的内容会被人为地给予较高评价,而来自不知名用户名的内容则会被人为地给予较低评价。对于读者,我们的研究结果表明存在两种类型:轻度偏见型和重度偏见型。随后对推特(Twitter)作者用户名的分析揭示了这种偏见背后的用户名属性,包括性别因素、用户名类型(个人还是组织)以及与话题的相关程度。我们讨论了我们的研究成果如何能够为内容分发商和搜索引擎在利用和展示微博内容方面提供指导。
课程简介: Bias can be defined as selective favoritism exhibited by human beings when posed with a task of decision making across multiple options. Online communities present plenty of decision making opportunities to their users. Users exhibit biases in their attachments, voting and ratings and other tasks of decision making. We study bias amongst microblog users due to the value of an author’s name. We describe the relationship between name value bias and number of followers, and cluster authors and readers based on patterns of bias they receive and exhibit, respectively. For authors we show that content from known names (e.g., @CNN) is rated artificially high, while content from unknown names is rated artificially low. For readers, our results indicate that there are two types: slightly biased, heavily biased. A subsequent analysis of Twitter author names revealed attributes of names that underlie this bias, including effects for gender, type of name (individual versus organization), and degree of topical relevance. We discuss how our work can be instructive to content distributors and search engines in leveraging and presenting microblog content.
关 键 词: 偏见; 选择性偏袒; 在线社区
课程来源: videolectures
数据采集: 2025-03-22:yuhongrui
最后编审: 2025-03-22:yuhongrui
阅读次数: 6