Abstract
Public entities such as companies and politicians increasingly use online social networks to communicate directly with their constituencies. Often, this public messaging is aimed at aligning the entity with a particular cause or issue, such as the environment or public health. However, as a consumer or voter, it can be difficult to assess an entity's true commitment to a cause based on public messaging. In this paper, we present a text classification approach to categorize a message according to its commitment level toward a cause. We then compare the volume of such messages with external ratings based on entities' actions (e.g., a politician's voting record with respect to the environment or a company's rating from environmental non-profits). We find that by distinguishing between low- and high- level commitment messages, we can more reliably identify truly committed entities. Furthermore, by measuring the discrepancy between classified messages and external ratings, we can identify entities whose public messaging does not align with their actions, thereby providing a methodology to identify potentially 'inauthentic' messaging campaigns.
Original language | English (US) |
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Title of host publication | Proceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 |
Editors | Raju Gottumukkala, George Karypis, Vijay Raghavan, Xindong Wu, Lucio Miele, Srinivas Aluru, Xia Ning, Guozhu Dong |
Publisher | IEEE Computer Society |
Pages | 1050-1057 |
Number of pages | 8 |
ISBN (Electronic) | 9781538614808 |
DOIs | |
State | Published - Dec 15 2017 |
Event | 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 - New Orleans, United States Duration: Nov 18 2017 → Nov 21 2017 |
Publication series
Name | IEEE International Conference on Data Mining Workshops, ICDMW |
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Volume | 2017-November |
ISSN (Print) | 2375-9232 |
ISSN (Electronic) | 2375-9259 |
Other
Other | 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 |
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Country/Territory | United States |
City | New Orleans |
Period | 11/18/17 → 11/21/17 |
Funding
Anonymous reviewers helped improve this paper. This research was funded in part by the National Science Foundation under grants #IIS-1526674 and #IIS-1618244. This research was funded in part by the National Science Foundation under grants #IIS-1526674 and #IIS-1618244.
Keywords
- Cause
- Commitment
- Public messaging
ASJC Scopus subject areas
- Computer Science Applications
- Software