Are Words Commensurate with Actions? Quantifying Commitment to a Cause from Online Public Messaging
This addresses the challenge for consumers and voters in evaluating the authenticity of public messaging by companies and politicians, though it is incremental as it applies existing text classification methods to a new domain.
The paper tackles the problem of assessing entities' true commitment to causes from online public messaging by developing a text classification approach to categorize messages by commitment level and comparing them with external action-based ratings. The result shows that distinguishing between low- and high-commitment messages helps reliably identify committed entities and measure discrepancies to detect inauthentic campaigns.
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.