Abstract
Online misinformation is primarily spread by humans deciding to do so. We therefore seek to understand the factors making this content compelling and, ultimately, driving online sharing. Fuzzy-Trace Theory, a leading account of decision making, posits that humans encode stimuli, such as online content, at multiple levels of representation; namely, gist, or bottom-line meaning, and verbatim, or surface-level details. Both of these levels of representation are expected to contribute independently to online information spread, with the effects of gist dominating. Important aspects of gist in the context of online content include the presence of a clear causal structure, and semantic coherence – both of which aid in meaning extraction. In this paper, we test the hypothesis that causal and semantic coherence are associated with online sharing of misinformative social media content using Coh-Metrix – a widely-used set of psycholinguistic measures. Results support Fuzzy-Trace Theory’s predictions regarding the role of causally- and semantically-coherent content in promoting online sharing and motivate better measures of these key constructs.
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Notes
- 1.
Finding local and global coherence is based on the work by Trabasso and van den Broek [22] on causal network model. The idea is that causal connections between text units are established at three levels. First level: organizing events in episodes, or GAO (goal, action, outcome) structures. Second level: linking GAOs to adjacent GAOs in a linear sequence. And third level: linking GAOs, even distant ones, in a global causal network.
- 2.
- 3.
Our inputs to the Coh-Metrix are all plain text stored in .txt file format.
- 4.
Normal probability plots show that residuals follow a normal distribution, see Fig. 1.
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Acknowledgement
We would like to sincerely thank the Coh-Metrix team for their invaluable advice and assistance with using their tool for our analysis.
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Hosseini, P., Diab, M., Broniatowski, D.A. (2019). Does Causal Coherence Predict Online Spread of Social Media?. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_19
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