Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2702613.2732787 | CHI Extended Abstracts |
Keywords | Field | DocType |
user/machine systems,text analytics,well-being,social computing,sentiment analysis,human-computer interaction,human computer interaction | Data science,World Wide Web,Social media,News aggregator,Sentiment analysis,Participatory management,Computer science,Well-being,Human–computer interaction,Emotive,Social computing,Community management | Conference |
Citations | PageRank | References |
4 | 0.57 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Lindner | 1 | 4 | 0.91 |
| Margeret Hall | 2 | 8 | 3.02 |
| Claudia Niemeyer | 3 | 6 | 0.95 |
| Simon Caton | 4 | 159 | 16.20 |