BeWell:主动社区管理的情感聚合器

Andreas Lindner, M. Hall, C. Niemeyer, Simon Caton
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引用次数: 16

摘要

可以不引人注意地收集精细的局部信息,以评估公众情绪,作为衡量政策影响的优越指标。这些信息已经很丰富,可以通过在线社交媒体获得。缺失的环节是一个严格的、匿名的、开源的工件,它向涉众和成员提供反馈。为了解决这个问题,BeWell提出了一种不显眼的、低延迟的多分辨率测量方法,用于观察、分析和建模社区动态。为了评估公共福祉,德国一所大型公立大学的42个Facebook页面使用基于词典的文本分析程序LIWC进行了分析。我们在整个样本中建立了情感话语的基线,并在此概念验证实施中检测重大校园范围内的事件,然后讨论未来的迭代,包括社区仪表板和参与式管理计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BeWell: A Sentiment Aggregator for Proactive Community Management
Granular, localized information can be unobtrusively gathered to assess public sentiment as a superior measure of policy impact. This information is already abundant and available via Online Social Media. The missing link is a rigorous, anonymized and open source artefact that gives feedback to stakeholders and constituents. To address this, BeWell, an unobtrusive, low latency multi-resolution measurement for the observation, analysis and modelling of community dynamics, is proposed. To assess communal well-being, 42 Facebook pages of a large public university in Germany are analyzed with a dictionary-based text analytics program, LIWC. We establish the baseline of emotive discourse across the sample, and detect significant campus-wide events in this proof of concept implementation, then discuss future iterations including a community dashboard and a participatory management plan.
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