Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues

Graells-Garrido, Eduardo; Baeza–Yates, Ricardo; Lalmas, Mounia

doi:10.1145/3394231.3397907

Cited by 24 publications

(21 citation statements)

References 36 publications

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“…In one study, researchers looked at Twitter names and bios, uncovering stark differences in the emoji use of groups supporting and opposed to white nationalism (Hagen et al, 2019). Graells-Garrido et al (2020) found that in two South American countries, different colour variations of heart emoji indicated users' opinions about abortions: tweets containing the green heart emoji ' ' were more likely to convey support of women's rights, while the blue heart emoji ' ' was more associated with stronger restrictions of abortions. In another study, researchers explored differences in emoji usage across cultures, finding that users from western countries tend to use more emoji than users from eastern countries (Guntuku et al, 2019).…”

Section: Self-representation In Emojimentioning

confidence: 99%

Emoji and Self-Identity in Twitter Bios

Longinos¹,

Wilson²,

Magdy³

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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Emoji are widely used to express emotions and concepts on social media, and prior work has shown that users' choice of emoji reflects the way that they wish to present themselves to the world. Emoji usage is typically studied in the context of posts made by users, and this view has provided important insights into phenomena such as emotional expression and selfrepresentation. In addition to making posts, however, social media platforms like Twitter allow for users to provide a short bio, which is an opportunity to briefly describe their account as a whole. In this work, we focus on the use of emoji in these bio statements. We explore the ways in which users include emoji in these self-descriptions, finding different patterns than those observed around emoji usage in tweets. We examine the relationships between emoji used in bios and the content of users' tweets, showing that the topics and even the average sentiment of tweets varies for users with different emoji in their bios. Lastly, we confirm that homophily effects exist with respect to the types of emoji that are included in bios of users and their followers.

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Section: Self-representation In Emojimentioning

confidence: 99%

Emoji and Self-Identity in Twitter Bios

Longinos¹,

Wilson²,

Magdy³

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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“…Extending our analysis to incorporate other platforms that each have different demographics and purposes can help to broaden the generalisability of our results, as well as integrating these data with information from traditional survey sources. An emerging approach to improve public opinion estimates based on Twitter data is the use of demographic sample weights (McCormick et al, 2017), which may need prediction of demographic attributes from Twitter users' information profiles (Graells-Garrido et al, 2020) as these attributes are not always or rarely available. As this area develops and best practices are established, the design and implementation of sample weights represents a fruitful area for future research.…”

Section: Challenges and Limitationsmentioning

confidence: 99%

Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic

Rowe¹,

Mahony²,

Graells-Garrido³

et al. 2021

Preprint

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In 2020, the world faced an unprecedented challenge to tackle and understand the spread and impacts of COVID-19. Large-scale coordinated efforts have been dedicated to understand the global health and economic implicationsof the pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations, particularlymigrants and individuals of Asian descent, has largely been neglected. Understanding public attitudes towardsmigration is essential to counter discrimination against immigrants and promote social cohesion. Traditional datasources to monitor public opinion – ethnographies, interviews, and surveys – are often limited due to smallsamples, high cost, low temporal frequency, slow collection, release and coarse spatial resolution. New forms ofdata, particularly from social media, can help overcome these limitations. While some bias exists, social mediadata are produced at an unprecedented temporal frequency, geographical granularity, are collected globally andaccessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this paperaims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemicin Germany, Italy, Spain, the United Kingdom and the United States. Results show an increase of migration-relatedTweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence ofa significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset bycomparable increases in positive messages. Additionally, we presented evidence of growing social polarisationconcerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

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“…Each formation theory defines an attitude, and, in cases where the classifier confidence is low, we define an undisclosed stance to account for participation in the debate without disclosing attitude [51]. Particularly, we build upon our previous work to classify users into attitudes as political stances using a tree-based classifier [17]. As stances, attitudes are not always explicit, and thus, they must be predicted.…”

Section: Social Media Analysis In the Study Of Human Behaviourmentioning

confidence: 99%

“…On the one hand, stance can be predicted using network interactions, based on the assumption that like-minded individuals are more likely to interact [52,53]. On the other hand, lexical analyses have shown to allow predicting stance as vocabularies within stances tend to have strongly associated words [54,55], and even other non-textual cues such as emojis [17].…”

Section: Social Media Analysis In the Study Of Human Behaviourmentioning

confidence: 99%

“…Social media platforms, for example microblogging sites such as Twitter, offer a major source of real-time information to understand and quantify attitudes towards immigration. Twitter not only serves as a public forum to exchange opinions and ideas on a broad set of societal issues, including political events [15], abortion legislation [16,17] and migration [18][19][20][21][22], it also shapes the opinions of its users [23]. However, Twitter has also enabled the spread of misinformation and negative rhetoric fueling hate speech [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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A Framework to Understand Attitudes towards Immigration through Twitter

2021

Self Cite

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Understanding public opinion towards immigrants is key to prevent acts of violence, discrimination and abuse. Traditional data sources, such as surveys, provide rich insights into the formation of such attitudes; yet, they are costly and offer limited temporal granularity, providing only a partial understanding of the dynamics of attitudes towards immigrants. Leveraging Twitter data and natural language processing, we propose a framework to measure attitudes towards immigration in online discussions. Grounded in theories of social psychology, the proposed framework enables the classification of users’ into profile stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users’ content and temporal stance trends. We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010. We found that the negative attitude group of users is smaller than the positive group, and that both attitudes have different distributions of the volume of content. Both types of attitudes show fluctuations over time that seem to be influenced by news events related to immigration. Accounts with negative attitudes use arguments of labour competition and stricter regulation of immigration. In contrast, accounts with positive attitudes reflect arguments in support of immigrants’ human and civil rights. The framework and its application can inform policy makers about how people feel about immigration, with possible implications for policy communication and the design of interventions to improve negative attitudes.

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Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues

Cited by 24 publications

References 36 publications

Emoji and Self-Identity in Twitter Bios

Emoji and Self-Identity in Twitter Bios

Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic

A Framework to Understand Attitudes towards Immigration through Twitter

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