(Political) Internet Memes as vectors of information: a multimethod approach

Lead Research Organisation: University of Nottingham
Department Name: School of Computer Science


This research aims at understanding what effects internet memes have on social media users. This will be done by analysing Internet Memes (IMs) in general and then focusing on the effects that Political Internet Memes (PIMs) have on users. This focus is important as it has been suggested in the literature that this specific type of IM has effects on online political discourse and digital politics. Thus, the proposed PhD will:

- significantly contribute to the emerging literature on memes;
- construct tools and apply novel methods to the analysis of IMs and PIMs;
- offer insights on online political discourse, information and opinion spread; and
- contribute to the literature in computer science, digital politics, digital communication, and psychology.

The proposed methodology and planned activities work in an iterative manner: the insights gained from each of the stages will shape and inform the next stage's design and hypotheses.

In order to address the research questions, we need to first address a critical problem in Internet Memes research: systematicity in the study of memes. This will be addressed by creating a meme analysis tool and making the analysis of memes automatic. The meme analysis tool this PhD aims at constructing combines multiple Deep Neural Networks to extract the information and features of the IM. This has already been shown to be possible by Beskow et al. The meme analysis tool will seek to create a supervised multimodal deep learning approach to the multimedia analysis of internet memes. Scholars have already produced research on analysing IMs with DNNs in various media formats including text, images and videos.

Having created the tool and explored its use, this research will then proceed into employing experimental and quasi-experimental designs to assess what effects PIMs and IMs have on users in terms of political knowledge, opinion formation and change, attitudes, information seeking intentions, political cynicism and emotional states. Subsequently, we will craft a simulation of a social media feed and analyse user behaviour in response to the feed both on the simulation device and with the use of eye-tracking technology.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023305/1 30/09/2019 30/03/2028
2439906 Studentship EP/S023305/1 30/09/2020 29/09/2024 Giovanni Schiazza