Are Words Commensurate with Actions? Quantifying Commitment to a Cause from Online Public Messaging

October 06, 2020 ยท Declared Dead ยท ๐Ÿ› 2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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Authors Zhao Wang, Jennifer Cutler, Aron Culotta arXiv ID 2010.02466 Category cs.CL: Computation & Language Citations 1 Venue 2017 IEEE International Conference on Data Mining Workshops (ICDMW) Last Checked 3 months ago
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.
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