Emoticons vs. Emojis on Twitter: A Causal Inference Approach
By Umashanthi Pavalanathan, Jacob Eisenstein. 2015
99 citations recorded by the source corpus.
Online writing lacks the non-verbal cues present in face-to-face communication, which provide additional contextual information about the utterance, such as the speaker's intention or affective state. To fill this void, a number of orthographic features, such as emoticons, expressive lengthening, and non-standard punctuation, have become popular in social media services including Twitter and Instagram. Recently, emojis have been introduced to social media, and are increasingly popular. This raises the question of whether these predefined pictographic characters will come to replace earlier orthographic methods of paralinguistic communication. In this abstract, we attempt to shed light on this question, using a matching approach from causal inference to test whether the adoption of emojis causes individual users to employ fewer emoticons in their text on Twitter.
Subfields: Artificial Intelligence, 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
- Carlos Castillo
- Shuo Chang
- Fernando Diaz
- Lee Chapman
- Loreen Terveen
- H. Roberts
- Brent J. Hecht
- Emre Kıcıman
In Human-Computer Interaction
Citations in the corpus
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Further, in the case of Twitter, much research relies on APIs that give access to at most 1% of the data (González-Bailón et al., 2014a; Joseph et al., 2014; Morstatter et al., 2014).”
- "Blissfully Happy" or "Ready toFight": Varying Interpretations of Emoji (2016, background): “Emoji are often described as a successor to emoticons (e.g., Novak et al. 2015), and Pavalanathan and Eisenstein (2016) found that while emoticons are decreasing in popularity on Twitter, emoji are increasing in popularity and seem to be replacing, not complementing, emoticons.”
- SemEval 2018 Task 2: Multilingual Emoji Prediction (2018, unknown): “In the application avenue, emoji similarity has been studied for proposing efficient keyboard emoji organization, essentially for placing similar emojis close in the keyboard (Pohl et al., 2017).”
- The value of Twitter data for determining the emotional responses of people to urban green spaces: A case study and critical evaluation (2019, methodology): “Additionally, while the large lexicon used provides robustness for scoring 396 words, it does not include emojis which are increasingly common ways to express sentiment in short 397 social media posts (Pavalanathan and Eisenstein, 2015).”