The Shadowy Lives of Emojis: An Analysis of a Hacktivist Collective's
Use of Emojis on Twitter
- URL: http://arxiv.org/abs/2105.03168v1
- Date: Fri, 7 May 2021 11:21:04 GMT
- Title: The Shadowy Lives of Emojis: An Analysis of a Hacktivist Collective's
Use of Emojis on Twitter
- Authors: Keenan Jones, Jason R. C. Nurse, Shujun Li
- Abstract summary: We present the first examination of emoji usage by hacktivist groups via a study of the Anonymous collective on Twitter.
This research aims to identify whether Anonymous affiliates have evolved their own approach to using emojis.
- Score: 6.510061176722249
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Emojis have established themselves as a popular means of communication in
online messaging. Despite the apparent ubiquity in these image-based tokens,
however, interpretation and ambiguity may allow for unique uses of emojis to
appear. In this paper, we present the first examination of emoji usage by
hacktivist groups via a study of the Anonymous collective on Twitter. This
research aims to identify whether Anonymous affiliates have evolved their own
approach to using emojis. To do this, we compare a large dataset of Anonymous
tweets to a baseline tweet dataset from randomly sampled Twitter users using
computational and qualitative analysis to compare their emoji usage. We utilise
Word2Vec language models to examine the semantic relationships between emojis,
identifying clear distinctions in the emoji-emoji relationships of Anonymous
users. We then explore how emojis are used as a means of conveying emotions,
finding that despite little commonality in emoji-emoji semantic ties, Anonymous
emoji usage displays similar patterns of emotional purpose to the emojis of
baseline Twitter users. Finally, we explore the textual context in which these
emojis occur, finding that although similarities exist between the emoji usage
of our Anonymous and baseline Twitter datasets, Anonymous users appear to have
adopted more specific interpretations of certain emojis. This includes the use
of emojis as a means of expressing adoration and infatuation towards notable
Anonymous affiliates. These findings indicate that emojis appear to retain a
considerable degree of similarity within Anonymous accounts as compared to more
typical Twitter users. However, their are signs that emoji usage in Anonymous
accounts has evolved somewhat, gaining additional group-specific associations
that reveal new insights into the behaviours of this unusual collective.
Related papers
- Semantics Preserving Emoji Recommendation with Large Language Models [47.94761630160614]
Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text.
We propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text.
arXiv Detail & Related papers (2024-09-16T22:27:46Z) - EmojiLM: Modeling the New Emoji Language [44.23076273155259]
We develop a text-emoji parallel corpus, Text2Emoji, from a large language model.
Based on the parallel corpus, we distill a sequence-to-sequence model, EmojiLM, which is specialized in the text-emoji bidirectional translation.
Our proposed model outperforms strong baselines and the parallel corpus benefits emoji-related downstream tasks.
arXiv Detail & Related papers (2023-11-03T07:06:51Z) - On the Context-Free Ambiguity of Emoji: A Data-Driven Study of 1,289
Emojis [28.04805745702487]
We collect a crowdsourced dataset of one-word emoji descriptions for 1,289 emojis presented to participants with no surrounding text.
We find that with 30 annotations per emoji, 16 emojis are completely unambiguous, whereas 55 emojis are so ambiguous that their descriptions are indistinguishable from randomly chosen descriptions.
arXiv Detail & Related papers (2022-01-17T09:33:29Z) - Emojich -- zero-shot emoji generation using Russian language: a
technical report [52.77024349608834]
"Emojich" is a text-to-image neural network that generates emojis using captions in Russian language as a condition.
We aim to keep the generalization ability of a pretrained big model ruDALL-E Malevich (XL) 1.3B parameters at the fine-tuning stage.
arXiv Detail & Related papers (2021-12-04T23:37:32Z) - Emoji-aware Co-attention Network with EmoGraph2vec Model for Sentiment
Anaylsis [9.447106020795292]
We propose a method to learn emoji representations called EmoGraph2vec and design an emoji-aware co-attention network.
Our model designs a co-attention mechanism to incorporate the text and emojis, and integrates a squeeze-and-excitation block into a convolutional neural network.
Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets.
arXiv Detail & Related papers (2021-10-27T08:01:10Z) - Black or White but never neutral: How readers perceive identity from
yellow or skin-toned emoji [90.14874935843544]
Recent work established a connection between expression of identity and emoji usage on social media.
This work asks if, as with language, readers are sensitive to such acts of self-expression and use them to understand the identity of authors.
arXiv Detail & Related papers (2021-05-12T18:23:51Z) - Identity Signals in Emoji Do not Influence Perception of Factual Truth
on Twitter [90.14874935843544]
Prior work has shown that Twitter users use skin-toned emoji as an act of self-representation to express their racial/ethnic identity.
We test whether this signal of identity can influence readers' perceptions about the content of a post containing that signal.
We find that neither emoji nor profile photo has an effect on how readers rate these facts.
arXiv Detail & Related papers (2021-05-07T10:56:19Z) - Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018 [66.28665205489845]
We offer the first longitudinal study of how emoji semantics changes over time, applying techniques from computational linguistics to six years of Twitter data.
We identify five patterns in emoji semantic development and find evidence that the less abstract an emoji is, the more likely it is to undergo semantic change.
To aid future work on emoji and semantics, we make our data publicly available along with a web-based interface that anyone can use to explore semantic change in emoji.
arXiv Detail & Related papers (2021-05-03T13:35:10Z) - A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and
Application Source [1.6818451361240172]
We showcase the importance of using Twitter features to help the model understand the sentiment involved and hence to predict the most suitable emoji for the text.
Our data analysis and neural network model performance evaluations depict that using hashtags and application sources as features allows to encode different information and is effective in emoji prediction.
arXiv Detail & Related papers (2021-03-14T03:05:04Z) - Are Emojis Emotional? A Study to Understand the Association between
Emojis and Emotions [37.86739837901986]
We seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings.
We additionally conduct experiments to assess to what extent such associations can be inferred from existing data, such as similar associations can be predicted for a larger set of emojis.
arXiv Detail & Related papers (2020-05-02T04:04:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.