0


Facebook 中自我表达的维度Facebook 中自我表达的维度

Dimensions of Self-Expression in Facebook
课程网址: https://videolectures.net/videos/icwsm2013_chen_filtering_tweets  
主讲教师: Jilin Chen
开课单位: 信息不详。欢迎您在右侧留言补充。
开课时间: 2014-04-03
课程语种: 英语
中文简介:
许多消费品牌都有客户关系代理,可以直接在社交流(例如 Twitter)上吸引固执己见的消费者。为了帮助代理找到固执己见的消费者,社交流监控工具提供了基于关键字的过滤器,这些过滤器通常过于粗粒度而无效。在这项工作中,我们介绍了 CrowdE,这是一个基于 Twitter 的过滤系统,可帮助代理通过特定于品牌的智能过滤器找到有主见的客户。为了最大限度地减少每个品牌在创建这些品牌特定筛选条件时的工作量,该系统使用了一个常见的支持众包的流程,该流程通过机器学习对众包标签的推文创建筛选条件。我们验证了 crowd 标签的质量和根据标签构建的筛选算法的性能。用户评估进一步表明,CrowdE 的智能过滤器提高了任务性能,与当前社交流监控工具中基于关键字的过滤器相比,它通常受到用户的青睐。
课程简介: Many consumer brands have customer relationship agents that directly engage opinionated consumers on social streams, such as Twitter. To help agents find opinionated consumers, social stream monitoring tools provide keyword-based filters, which are often too coarse-grained to be effective. In this work, we introduce CrowdE, a Twitter-based filtering system that helps agents find opinionated customers through brand-specific intelligent filters. To minimize per-brand effort in creating these brand-specific filters, the system used a common crowd-enabled process that creates the filters through machine learning over crowd-labeled tweets. We validated the quality of the crowd labels and the performance of the filter algorithms built from the labels. A user evaluation further showed that CrowdE's intelligent filters improved task performance and were generally preferred by users in comparison to keyword-based filters in current social stream monitoring tools.
关 键 词: CrowdE; 社交流监控; 智能过滤器
课程来源: videolectures
数据采集: 2025-05-16:yuhongrui
最后编审: 2025-05-16:yuhongrui
阅读次数: 2