How Gender and Skin Tone Modifiers Affect Emoji Semantics in Twitter

By Francesco Barbieri, José Camacho-Collados. 2018

21 citations recorded by the source corpus.

In this paper we analyze the use of emojis in social media with respect to gender and skin tone. By gathering a dataset of over twenty two million tweets from United States some findings are clearly highlighted after performing a simple frequency-based analysis. Moreover, we carry out a semantic analysis on the usage of emojis and their modifiers (e.g. gender and skin tone) by embedding all words, emojis and modifiers into the same vector space. Our analyses reveal that some stereotypes related to the skin color and gender seem to be reflected on the use of these modifiers. For example, emojis representing hand gestures are more widely utilized with lighter skin tones, and the usage across skin tones differs significantly. At the same time, the vector corresponding to the male modifier tends to be semantically close to emojis related to business or technology, whereas their female counterparts appear closer to emojis about love or makeup.

Subfields: Artificial Intelligence, Literature and Literary Theory, Natural Language Processing, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

In Human-Computer Interaction

Citations in the corpus

Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models (2019, background): “Popular resources like Wikipedia [Wagner et al., 2015], news articles [Ross and Carter, 2011], Twitter [Barbieri and Camacho-Collados, 2018] possess gender bias in one or the other form.”
SemEval 2018 Task 2: Multilingual Emoji Prediction (2018, background): “For example, Barbieri and Camacho-Collados (2018) show that emoji modiﬁers can affect the semantics of emojis (they looked specifically into skin tones and gender).”