Overview
Political polarization and declining trust in institutions are driving societal destabilization and radicalization. Recently there has been increased interest in online misinformation intervention and deterrence, for example through the use of machine learning on language use. We argue that addressing crises in the information environment will require a sharper situational awareness and a deeper understanding of how beliefs emerge and crystallize, as well as greater connectivity in the work of teams and organizations in order to reduce the effects of bias and partisanship in collection and analysis. Image memes play an increasingly important role in public sensemaking and discourse and the emergence of public beliefs. Despite their significance, image memes have proven to be a very difficult category of artifact to collect, classify, and analyze in aggregate. In this white paper, the function and form of image memes are discussed, the challenges of performing image meme collection and analysis within the context of emergent, interdisciplinary teams are detailed, and requirements and recommendations for alleviating these challenges are offered.
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Methods and contributions
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Findings and contributions
- Argues that image memes function as quasi-arguments whose parsimonious form hides warrants, so interventions should target spurious evidence and hidden warrants.
- Argues that framing the information crisis as true versus false information has not been effective at regaining trust of disaffected audiences.
- Concludes that current image meme collection by emergent teams falls short of needs even though the needed affordances are within technological reach.
- Recommends system requirements such as on-site information, dynamic web annotations, and proximal collection and tagging affordances.
- Proposes digital ecosystem and API economy approaches as a viable route to many of the challenges, while noting data standardization and integration remain problems.
Methods
- Toulmin claim/evidence/warrant annotation of example image memes — Applies the Toulmin argument model to worked examples (e.g. a NASA meme, 'Condescending Wonka') to expose implicit warrants.
- Archetypal-phase vignettes of three emergent-team collection approaches — Describes Haphazard, Methodological, and Automated Collection and Analysis approaches, drawn from the authors' experiences, with challenge tables.
- Russo-Ukrainian War memes as illustrative examples — Uses memes about the ongoing Russo-Ukrainian War to illustrate how memetic analysis can reveal shared public beliefs.
- Extension of the Digital Rhetorical Ecosystem (DRE3) framework — Builds on the authors' prior computational framework combining rhetorical analysis with an ecosystem approach.
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- ECOMEME2-v1.0.pdf PDF · 4.05 MiB · extracted-text source
Citation
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