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.
Original language | English |
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Title of host publication | CHI EA '15 |
Editors | Bo Begole, Jinwoo Kim, Kori Inkpen, Woontack Woo |
Place of Publication | Seoul Republic of Korea |
Publisher | ACM Press |
Pages | 1055 - 1060 |
ISBN (Print) | 9781450331463 |
DOIs | |
Publication status | Published - 2015 |
Austrian Classification of Fields of Science and Technology (ÖFOS)
- 102