Community Poll: Externalizing Public Sentiments in Social Media in a Local Community Context - Shih, Patrick; Han, Kyungsik; Carroll, John (Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2014)
Social media platforms such as Twitter and Facebook are commonly used to disseminate up-to-date news information, but they also contain a lot of noise and irrelevant content. The contents of social media platforms are typically filtered by followship or friendship oriented relationships, and is almost always driven by trending news topics at the national scale, making it difficult for users to gather useful information that is most pertinent to a local community context. Research has utilized content analysis techniques to gain insights on the sentiment expressed about political topics on social media sites. However, there has been little attempt to understand how users would perceive this information if opinions and sentiments about news topics were externalized and made aware to them. We designed Community Poll, a smartphone application that aggregates local news feeds with relevant tweets about the local news topics. A Public Attitude Meter is calculated based on the sentiment score of the tweets for each of the local news topic presented in the system. We conducted a 2-week deployment with 16 users about their perception of the system. The users reported that Community Poll helps them digest locally relevant news topics, and quickly gather public opinions associated with the topics. They reported that being aware of public sentiment encouraged them to more actively participate in discussions on social media. Curiosity about a score-based representation is an important element that drove them to consume local news topics that they wouldn’t otherwise be exposed to. Interestingly, although being aware of public sentiment served to reaffirm people’s positions on the local topics, users expressed concerns about how sentiment awareness might bias other people’s judgments regarding news topics.