Hashtags as Signals of Political Identity: #BlackLivesMatter and #AllLivesMatter

Powell, Maia; Kim, Arnold D.; Smaldino, Paul E.

doi:10.31235/osf.io/tqs2x

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…There are ways to reduce ambiguity noise in a social debate, for example, by enforcing more transparent and rigorous communication [25] or by fostering the use of clearly identifiable markers [70], such that socially transmitted opinions are more representative of the actual opinions (e.g. by wearing clothes items or using hashtags associated with the support for a particular opinion [83]). Our study, however, supports literature promoting the benefits of noise or ambiguity in communication to foster agreement across society (e.g.…”

Section: Discussionmentioning

confidence: 99%

Noise and opinion dynamics: how ambiguity promotes pro-majority consensus in the presence of confirmation bias

Steiglechner,

Keijzer,

E. Smaldino

et al. 2024

R. Soc. Open Sci.

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Opinion dynamics are affected by cognitive biases and noise. While mathematical models have focused extensively on biases, we still know surprisingly little about how noise shapes opinion patterns. Here, we use an agent-based opinion dynamics model to investigate the interplay between confirmation bias—represented as bounded confidence—and different types of noise. After analysing where noise can enter social interaction, we propose a type of noise that has not been discussed so far, ambiguity noise. While previously considered types of noise acted on agents either before, after or independent of social interaction, ambiguity noise acts on communicated messages, assuming that socially transmitted opinions are inherently noisy. We find that noise can induce agreement when confirmation bias is moderate, but different types of noise require quite different conditions for this effect to occur. An application of our model to the climate change debate shows that at just the right mix of confirmation bias and ambiguity noise, opinions tend to converge to high levels of climate change concern. This result is not observed with the other types. Our findings highlight the importance of considering and distinguishing between the various types of noise and the unique role of ambiguity in opinion formation.

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Section: Discussionmentioning

confidence: 99%

Noise and opinion dynamics: how ambiguity promotes pro-majority consensus in the presence of confirmation bias

Steiglechner,

Keijzer,

E. Smaldino

et al. 2024

R. Soc. Open Sci.

Self Cite

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“…Group membership can thus become an important factor when evaluating the subjective opinions of others. Such differential evaluation of information, known as in-group bias, is a well-studied and persistent feature of human behaviour [16,26–32], although the impacts of this bias vary depending on the culture of the people involved [33] and the nature of the relevant social identities [34]. In-group bias usually reinforces similarities between in-group members [26] and, at the same time, draws like-minded people into the group [16].…”

Section: Introductionmentioning

confidence: 99%

Social identity bias and communication network clustering interact to shape patterns of opinion dynamics

Steiglechner,

Smaldino,

Moser

et al. 2023

J. R. Soc. Interface.

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Social influence aligns people's opinions, but social identities and related in-group biases interfere with this alignment. For instance, the recent rise of young climate activists (e.g. ‘Fridays for Future’ or ‘Last Generation’) has highlighted the importance of generational identities in the climate change debate. It is unclear how social identities affect the emergence of opinion patterns, such as consensus or disagreement, in a society. Here, we present an agent-based model to explore this question. Agents communicate in a network and form opinions through social influence. The agents have fixed social identities which involve homophily in their interaction preferences and in-group bias in their perception of others. We find that the in-group bias has opposing effects depending on the network topology. The bias impedes consensus in highly random networks by promoting the formation of echo chambers within social identity groups. By contrast, the bias facilitates consensus in highly clustered networks by aligning dispersed in-group agents across the network and, thereby, preventing the formation of isolated echo chambers. Our model uncovers the mechanisms underpinning these opposing effects of the in-group bias and highlights the importance of the communication network topology for shaping opinion dynamics.

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Detecting political biases of named entities and hashtags on Twitter

et al. 2023

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Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term “pro-choice” are likely to be liberal and people who use the term “pro-life” are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding’s polarity component and its semantic component. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications—including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler—and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.

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Hashtags as Signals of Political Identity: #BlackLivesMatter and #AllLivesMatter

Cited by 4 publications

References 32 publications

Noise and opinion dynamics: how ambiguity promotes pro-majority consensus in the presence of confirmation bias

Noise and opinion dynamics: how ambiguity promotes pro-majority consensus in the presence of confirmation bias

Social identity bias and communication network clustering interact to shape patterns of opinion dynamics

Detecting political biases of named entities and hashtags on Twitter

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