Tweeting and Retweeting: Gender Discrepancies in Discursive Political Engagement and Influence on Twitter

Hu, Lingshu; Kearney, Michael W.; Frisby, Cynthia M.

doi:10.1080/09589236.2021.1995340

Cited by 5 publications

(5 citation statements)

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“…Although both sending posts and likes can be considered indices of the strength of one's willingness to express themselves, they differ in terms of the level of engagement and involvement of others. Sending posts is a way of conveying one's ideas or sharing information (Dhir et al, 2019;Hu et al, 2023). Among the six groups, the informational group and the self-expressive group may have a higher likelihood of sending posts because, according to their language styles, these groups have a stronger motivation to share messages.…”

Section: Expression Desirementioning

confidence: 99%

“…Among the six groups, the informational group and the self-expressive group may have a higher likelihood of sending posts because, according to their language styles, these groups have a stronger motivation to share messages. However, expressing one's own ideas requires a higher level of intellectual effort compared to sharing information from other sources (Hu et al, 2023). That is to say, the informational group may outnumber the self-expressive group in sending posts.…”

Section: Expression Desirementioning

confidence: 99%

“…Discussing politics on social media is a form of discursive political participation. Differing from offline political participation, such as running for or holding political office, making political decisions, voting and protesting, discursive political participation centers on activities like writing, posting, sharing and commenting on various political issues (Hu et al ., 2023). These distinctions do not imply that discursive political participation is of lesser importance.…”

Section: Introductionmentioning

confidence: 99%

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Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter

2024

OIR

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PurposeThis study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.Design/methodology/approachThis study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.FindingsThrough the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.Practical implicationsThis study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.Social implicationsThis study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.Originality/valueThis study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

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Section: Expression Desirementioning

confidence: 99%

Section: Expression Desirementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter

2024

OIR

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“…The literature suggests that men and women use Twitter differently, in particular, when discussing political issues (e.g. Hu et al, 2021). Similarly, politicians' age and education may play a role, as these have been suggested to affect other dimensions of their Twitter outcomes, such as the number of followers (Keller and Kleinen-von Königslöw, 2018).…”

Section: Robustness: Politician and Account Characteristicsmentioning

confidence: 99%

Few voices, strong echo: Measuring follower homogeneity of politicians’ Twitter accounts

Rusche

2022

New Media & Society

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Politicians have discovered Twitter as a tool for political communication. If information provided by politicians is circulated in ideologically segregated user networks, political polarization may be fostered. Using network information on all 1.78 million unique followers of German Members of Parliament by October 2018, follower homogeneity across politicians and parties is measured. While the overall homogeneity is low, politicians of the AfD—a right-wing populist party—stand out with very homogeneous follower networks. These are largely driven by a small group of strongly committed partisans that make up around 7% of the party’s but around 55–75% of the average AfD politician’s followers. The findings add to the literature by showing potentially unequal distributions of network segregation on Twitter. Furthermore, they suggest that small groups of active users can multiply their influence online, which has important implications for future research on echo chambers and other online phenomena.

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“…Among all social media platforms, Twitter (now named "X") has unique features that encourage political discussion (Hu et al, 2023). For example, compared with Facebook, which is mainly used as a private social network for friends and families, or LinkedIn, which is primarily for professional networking, Twitter is more open, horizontal, and broadly-networked (Park, 2013).…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Method for Classifying Political Partisanship Using Deep Learning Models

2023

Social Science Computer Review

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Political partisanship constitutes a pivotal group identity that significantly influences individuals’ voting behaviors and shapes their ideological and cultural perspectives. While traditional surveys and experimental studies can directly capture political identity by asking the participants, this task has become intricate when employing digital trace data sourced from social media. Previous classification methods, attempting to infer political identity from users’ networks or textual content, suffered from limited efficiency or generalizability. In response, this study introduces a two-step method that utilizes deep learning models to enhance classification efficiency, generalizability, and interpretability. In the first step, two deep learning models, trained on 2.5 million tweets from 825 Congressional politicians in the U.S., achieved accuracy rates of 87.71% and 89.54%, respectively, in detecting politicians’ partisanships based on their individual tweets. Subsequently, in the second step, by employing a simple machine learning model that leverages the aggregated predicted values derived from the first-step models, accuracy rates of 94.92% and 96.61% were attained for identifying non-politician users’ political identities based off their 50 and 200 tweets, respectively. In addition, an attention mechanism was integrated into the deep learning model to assess the contribution of each word in the classification process.

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Tweeting and Retweeting: Gender Discrepancies in Discursive Political Engagement and Influence on Twitter

Cited by 5 publications

References 63 publications

Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter

Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter

Few voices, strong echo: Measuring follower homogeneity of politicians’ Twitter accounts

A Two-Step Method for Classifying Political Partisanship Using Deep Learning Models

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