Rumor Management in Online Networks


Rumor is a dynamic process of social interaction that can shape the way people understand, talk about and relate to events (Peterson and Gist 1951). Research shows that rumors often carry important information not available in other channels of communication. Managers need to be aware of how rumors are generated and dispersed to manage the implications of such informal information.

Foundational rumoring research, such as Allport and Postman 1947 and Peterson and Gist 1951, suggests that people will forward rumors that they are not fully convinced of if the core claims cohere with their sense of what happens in a given situation (Fine and Ellis 2010). The context of a rumor can also make it more likely to succeed, for example, if the claim is about something important to an individual or has potential impact on outcomes the person cares about.

In the online environment, participation in a rumor is a highly active form of engagement. In addition to sharing the rumor, participants add new evidence (e.g., a video of alleged election fraud), provide interpretations of evidence (e.g., a statistician interpreting vote count data), synthesize related rumors into larger narratives (e.g., connecting rumors about voting issues in different locations to larger claims of electoral fraud), and even correct false claims.

These dynamics have been accelerated through the social nature of online networks. As a result, it is becoming increasingly common for rumors to emerge and spread in bursts over time, and to move between different communities through structural bridges (Granovetter 1973).

How can managers be mindful of the role of rumor in their organisations? To help answer this question, we developed an analytical framework to categorize the various elements of a rumor. We define rumor as the collective reworking of existing information into an engaging story, which is then shared with others (Krafft and Donovan 2020). The framework is designed to allow for comparisons across different rumours in order to determine which rumours have the greatest potential for success. It includes categories for assessing how a rumour has been shaped through retelling, including sharpening and levelling of the details, adding or removing specific details, and using rhetorical features to create a compelling narrative. It also includes a category for identifying emotional appeals, such as those that invoke anger, outrage and self-righteousness, as well as those that villainize particular individuals or groups. The framework has been tested for reliability by having two independent coders analyze a set of five rumors, with Cohen’s Kappa indicating high levels of agreement. The framework can be used to identify rumours at all stages of the process, from initial generative stage through to its spread in the wider community. We also investigate how the effectiveness of debunking messages can be assessed by analyzing the peaks and troughs of a rumour’s development over time. This is illustrated in the figure below.