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BERTs of a feather: Studying inter- and intra-group communication via information theory and language models

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Abstract

When communicating, individuals alter their language to fulfill a myriad of social functions. In particular, linguistic convergence and divergence are fundamental in establishing and maintaining group identity. Quantitatively characterizing linguistic convergence is important when testing hypotheses surrounding language, including interpersonal and group communication. We provide a quantitative interpretation of linguistic convergence grounded in information theory. We then construct a computational model, built on top of a neural network model of language, that can be deployed to measure and test hypotheses about linguistic convergence in “big data.” We demonstrate the utility of our convergence measurement in two case studies: (1) showing that our measurement is indeed sensitive to linguistic convergence across turns in dyadic conversation, and (2) showing that our convergence measurement is sensitive to social factors that mediate convergence in Internet-based communities (specifically, r/MensRights and r/MensLib). Our measurement also captures differences in which social factors influence web-based communities. We conclude by discussing methodological and theoretical implications of this semantic convergence analysis.

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Notes

  1. However if we repeated this process for each word, the probability for each element of any utterance x based on a comparative sample can be thought of in terms of a multinomial distribution. For simplicity, we describe the sampling process in binary terms (yes/no between some x and some set y), and thus focus on the Bernoulli distribution for the rest of this paper.

  2. Entropy also decreases as two distributions become increasingly polar. If the probability of some term i is 1 in distribution a but the probability of i is 0 in distribution b the entropy of P(i|a) and P(i|b) is 0. This is because you can easily predict i in a by knowing that i in a is the opposite of what you have observed for i in b. Realistically, it is not clear how this condition could be met in language, however, and research shows that the language models that we will use to estimate entropy have a baseline similarity between any two randomly sampled word vectors \(> 0. \) and \(< 1.\) Ethayarajh (2019) thus rendering this condition impossible with our specific method.

  3. The method described in Rosen (2022) has two major differences from the current method. First, their measurement of convergence requires that the data contains samples from two groups with all individuals pre-labeled according to their group status in order to show that rhetoric is internally consistent within groups and inconsistent outside of them. Our current method does not require the presence of multiple groups in order to measure convergence. Second, their method requires the pre-selection of some set of key terms for analysis. Our method treats every word in an utterance as a unique experiment, and thus does not require any predefined lexicon in order to capture convergence.

  4. By no means is the use of a Gaussian Distribution the only way of converting a Cosine value to a probability. For example, one could go so far as to use \(\frac{1 + CoS(E_{xi}, E_{yj})}{2}\) to convert scalar Cosine Similarity (or CoS: which is the reciprocal of CoE) values to a ratio in terms of maximum similarity. We prefer the use of a Gaussian distribution here as a means of increasing the burden of proof required to claim two words mean the same thing based on the proximity of their word vectors, because of the way that the scale parameter \(\sigma \) can be used to increase penalties on word vectors that are dissimilar to one another.

  5. In truly unconstrained cases where one is comparing utterances to one another irrespective of interest in any one lexical item – i.e., comparing all sentences that invoke a specific phrase like “forced birth” – one should look for smaller sample sizes but a greater number of random samples taken in order to characterize the possible diversity of utterances in the data.

  6. https://github.com/saulalbert/CABNC

  7. The use of SBERT as a means of measuring semantic similarity (see: Alatawi, Sheth and Liu , 2023) has a major limitation however when compared to the method proposed here. Namely, SBERT is designed and trained to classify sentences with global semantic similarity-sentences that have been labeled as meaning the same thing, irrespective of their constituent components-as more similar to one another (Reimers & Gurevych, 2019). However, it is easy to imagine a case wherein individuals from a group, engaging with one another in ongoing discourse, should be expected to author utterances that imply wildly different meanings while leaning into group lexico-semantic norms for the subcomponents of their utterances.

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  1. Communication Studies Saddleback Community College, Mission Viejo, CA, USA

    Zachary P Rosen

  2. Department of Communication UCLA, Los Angeles, CA, USA

    Rick Dale

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Rosen, Z.P., Dale, R. BERTs of a feather: Studying inter- and intra-group communication via information theory and language models. Behav Res (2023). https://doi.org/10.3758/s13428-023-02267-2

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