Student Spotlight: Yan Xia, James Ackland, and Nikolay Petrov

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• Yan Xia (Aalto University), James Ackland (Cambridge), and Nikolay Petrov (Cambridge)
• Wednesday 06 March 2024, 15:00-16:00
• Nick Mackintosch Room, Department of Psychology, Downing Site.

If you have a question about this talk, please contact Yara Kyrychenko.

This talk is open to the general public.

Meeting ID: 329 287 585 675

Passcode: yKwfhf

Yan Xia (Aalto University):

Title: Integrated or Segregated? User Behavior Change after Cross-Party Interactions on Reddit

Abstract: It is a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbates political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community participation after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if their participation increases in communities of the opposing party, or in communities of their own party. We find that receiving a reply is significantly associated with increased user activity in both types of communities; when the reply is a cross-party one, the activity boost in cross-party communities is weaker. Nevertheless, compared with the case of receiving no reply, users are still significantly more likely to increase their participation in cross-party communities after receiving a cross-party reply. Our results therefore hint at a depolarization effect of cross-party interactions that better integrate users into discussions of the opposing side.

James Ackland (Cambridge):

Title: The Geographical Psychology of Ideological Misalignment

Abstract: Political psychologists have debated whether ideology is constructed from the top-down, by national-level parties and elites forming packages of beliefs to “sell” to voters (Downs, 1957); or from the bottom-up, by voters themselves aligning policy preferences with more fundamental social and psychological needs (Duckitt & Sibley, 2010). In this work, I assume that both processes coexist, and show how their interaction can explain some of the phenomena that characterise our modern politics. Of particular interest are places where bottom-up preferences are not matched by the top-down political offering. In Western Europe, this often means places where social conservatism exists alongside left-leaning economic preferences, in contrast to the pairing of social conservatism with a free-market ideology at the national level. In such places, I hypothesise that populist politics will be more successful, as measured by voting behaviour and political attitudes.

Nikolay Petrov (Cambridge):

Title: Limited ability of LLMs to simulate human psychological behaviours: an in-depth psychometric analysis

Abstract: The humanlike responses of Large Language Models (LLMs) have prompted social scientists to investigate whether LLMs can be used to simulate human participants in experiments, opinion polls and surveys. Of central interest in this line of research has been mapping out the psychological profile of LLMs by prompting them to respond to standardized questionnaires. The conflicting findings of this research are unsurprising given that going from LLMs’ text responses on surveys to mapping out underlying, or latent, traits is no easy task. To address this, we use psychometrics, the science of psychological measurement. In this study, we prompt OpenAI’s flagship models, GPT -3.5 and GPT -4, by asking them to assume different personas and respond to a range of standardized measures of personality constructs. We used two kinds of persona descriptions: either generic (5 random person descriptions) or specific (mostly demographics of actual humans from a large-scale human dataset). We found that using generic persona descriptions, more powerful models, such as GPT -4, show promising abilities to respond coherently, and similar to human norms, but both models failed miserably in assuming specific personas, described using demographic variables. We conclude that, currently, when LLMs are prompted to simulate specific human(s), they cannot represent latent traits and thus their responses fail to generalize across tasks.

This talk is part of the Social Psychology Seminar Series (SPSS) series.

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