Factors Influencing Emoji Usage in Smartphone Mediated Communications

An, Jiaxin; Li, Tian; Teng, Yifei; Zhang, Pengyi

doi:10.1007/978-3-319-78105-1_46

Cited by 23 publications

(19 citation statements)

References 15 publications

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“…Siebenhaar (2018) analyzed the usage of emoji in WhatsApp chats and found mixed results: While he reported emoji usage to be negatively associated with age in a Swiss chat corpus, he found no age differences in an initial analysis of the chat corpus we analyzed in the present study. In a similar manner, An et al (2018) did not find a consistent relationship of emoji usage with user age in WeChat messages. In line with theory that women experience and express emotions more often than men (Fabes & Martin, 1991;Kring & Gordon, 1998), previous research indicates that there are significant gender differences in the usage of emoji and emoticons.…”

Section: Linguistic Variations With Age and Gendermentioning

confidence: 76%

“…While user demographics play a role in the interpretation of emoji and emoticons (Butterworth et al, 2019;Herring & Dainas, 2020;Jaeger et al, 2017), research suggests that age and gender are also associated with the frequency and variety of their usage. Based on surveys (Jones et al, 2020;Pérez-Sabater, 2019;Prada et al, 2018) and real-world user data (An et al, 2018;Chen et al, 2018;Fullwood et al, 2013;Oleszkiewicz et al, 2017;Tossell et al, 2012;Wolf, 2000), researchers have found the usage of emoji and emoticons to systematically vary with demographics.…”

Section: Linguistic Variations With Age and Gendermentioning

confidence: 99%

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Age and gender in language, emoji, and emoticon usage in instant messages

Koch

Romero

Stachl

2022

Computers in Human Behavior

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Text is one of the most prevalent types of digital data that people create as they go about their lives. Digital footprints of people's language usage in social media posts were found to allow for inferences of their age and gender. However, the even more prevalent and potentially more sensitive text from instant messaging services has remained largely uninvestigated. We analyze language variations in instant messages with regard to individual differences in age and gender by replicating and extending the methods used in prior research on social media posts. Using a dataset of 309,229 WhatsApp messages from 226 volunteers, we identify unique age-and gender-linked language variations. We use cross-validated machine learning algorithms to predict volunteers' age (MAEMd = 3.95, rMd = 0.81, R 2 Md = 0.49) and gender (AccuracyMd = 85.7%, F1Md = 0.67, AUCMd = .82) significantly above baseline-levels and identify the most predictive language features. We discuss implications for psycholinguistic theory, present opportunities for application in author profiling, and suggest methodological approaches for making predictions from small text data sets. Given the recent trend towards the dominant use of private messaging and increasingly weaker user data protection, we highlight rising threats to individual privacy rights in instant messaging.

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Section: Linguistic Variations With Age and Gendermentioning

confidence: 76%

Section: Linguistic Variations With Age and Gendermentioning

confidence: 99%

Age and gender in language, emoji, and emoticon usage in instant messages

Koch

Romero

Stachl

2022

Computers in Human Behavior

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“…For example, [2] performs the first quantitative study to correlate Emoji usage to its semantic. Additionally, [5] analyzed messages of Wechat, and IM APP users in China, to learn the diversity of usage preferences of Emoji in frequency, type, and sentiment. The diversity and global usage of Emojis lead researchers to perform analysis of Emoji usage according to gender [10].…”

Section: Emoji Usage Analysismentioning

confidence: 99%

Inferring attributes with picture metadata embeddings

Pijani

Imine

Rusinowitch

2020

SIGAPP Appl. Comput. Rev.

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Users in online social networks are vulnerable to attribute inference attacks due to some published data. Thus, the picture owner's gender has a strong influence on individuals' emotional reactions to the photo. In this work, we present a graph-embedding approach for gender inference attacks based on pictures meta-data such as (i) alt-texts generated by Facebook to describe the content of images, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we apply a semi-supervised technique, node2vec, for learning a mapping of pictures meta-data to a low-dimensional vector space. Next, we study in this vector space the gender closeness of users who published similar photos and/or received similar reactions. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. Experimental results show that privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable.

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“…For example, [2] performs the first quantitative study to correlate Emoji usage to its semantic. Additionally, [4] analyzed messages of Wechat 4 , and IM APP users in China, to learn the diversity of usage preferences of Emoji in frequency, type, and sentiment. The diversity and global usage of Emojis lead researchers to perform analysis of Emoji usage according to gender [9].…”

Section: Emoji Usage Analysismentioning

confidence: 99%

You are what emojis say about your pictures

Pijani

Imine

Rusinowitch

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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The picture owner's gender has a strong influence on individuals' emotional reactions to the picture. In this study, we investigate gender inference attacks on their owners from pictures meta-data composed of: (i) alt-texts generated by Facebook to describe the content of pictures, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we study the correlation of picture owner gender with alt-text, and Emojis/Emoticons used by commenters when reacting to these pictures. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. We show that such a privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable.

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Factors Influencing Emoji Usage in Smartphone Mediated Communications

Cited by 23 publications

References 15 publications

Age and gender in language, emoji, and emoticon usage in instant messages

Age and gender in language, emoji, and emoticon usage in instant messages

Inferring attributes with picture metadata embeddings

You are what emojis say about your pictures

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