BeWell

A sentiment aggregator for proactive community management

Andreas Lindner, Margaret A Hall, Claudia Niemeyer, Simon Caton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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. Copyright is held by the author/owner(s).

Original languageEnglish (US)
Title of host publicationCHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationCrossings
PublisherAssociation for Computing Machinery
Pages1055-1060
Number of pages6
Volume18
ISBN (Electronic)9781450331463
DOIs
StatePublished - Apr 18 2015
Externally publishedYes
Event33rd Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2015 - Seoul, Korea, Republic of
Duration: Apr 18 2015Apr 23 2015

Other

Other33rd Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2015
CountryKorea, Republic of
CitySeoul
Period4/18/154/23/15

Fingerprint

Glossaries
Feedback

Keywords

  • Human-computer Interaction
  • Sentiment analysis
  • Social computing
  • Text analytics
  • Well-being

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Cite this

Lindner, A., Hall, M. A., Niemeyer, C., & Caton, S. (2015). BeWell: A sentiment aggregator for proactive community management. In CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings (Vol. 18, pp. 1055-1060). Association for Computing Machinery. https://doi.org/10.1145/2702613.2732787

BeWell : A sentiment aggregator for proactive community management. / Lindner, Andreas; Hall, Margaret A; Niemeyer, Claudia; Caton, Simon.

CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings. Vol. 18 Association for Computing Machinery, 2015. p. 1055-1060.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lindner, A, Hall, MA, Niemeyer, C & Caton, S 2015, BeWell: A sentiment aggregator for proactive community management. in CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings. vol. 18, Association for Computing Machinery, pp. 1055-1060, 33rd Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2015, Seoul, Korea, Republic of, 4/18/15. https://doi.org/10.1145/2702613.2732787
Lindner A, Hall MA, Niemeyer C, Caton S. BeWell: A sentiment aggregator for proactive community management. In CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings. Vol. 18. Association for Computing Machinery. 2015. p. 1055-1060 https://doi.org/10.1145/2702613.2732787
Lindner, Andreas ; Hall, Margaret A ; Niemeyer, Claudia ; Caton, Simon. / BeWell : A sentiment aggregator for proactive community management. CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings. Vol. 18 Association for Computing Machinery, 2015. pp. 1055-1060
@inproceedings{9dc2bec458dc44f8881e83928160f7ae,
title = "BeWell: A sentiment aggregator for proactive community management",
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. Copyright is held by the author/owner(s).",
keywords = "Human-computer Interaction, Sentiment analysis, Social computing, Text analytics, Well-being",
author = "Andreas Lindner and Hall, {Margaret A} and Claudia Niemeyer and Simon Caton",
year = "2015",
month = "4",
day = "18",
doi = "10.1145/2702613.2732787",
language = "English (US)",
volume = "18",
pages = "1055--1060",
booktitle = "CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - BeWell

T2 - A sentiment aggregator for proactive community management

AU - Lindner, Andreas

AU - Hall, Margaret A

AU - Niemeyer, Claudia

AU - Caton, Simon

PY - 2015/4/18

Y1 - 2015/4/18

N2 - 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. Copyright is held by the author/owner(s).

AB - 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. Copyright is held by the author/owner(s).

KW - Human-computer Interaction

KW - Sentiment analysis

KW - Social computing

KW - Text analytics

KW - Well-being

UR - http://www.scopus.com/inward/record.url?scp=84954237314&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84954237314&partnerID=8YFLogxK

U2 - 10.1145/2702613.2732787

DO - 10.1145/2702613.2732787

M3 - Conference contribution

VL - 18

SP - 1055

EP - 1060

BT - CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

ER -