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

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Highlights

• We analyze a dataset of 309,229 WhatsApp instant messages (N = 226).

• We identify age- and gender-linked variations in emoji, emoticon, and language usage.

• We use machine learning algorithms to significantly predict age and gender.

• We identify the most predictive language features.

• We discuss implications for user privacy in instant messaging.

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Abstract

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, R2Md = 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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Keywords

Age, Gender, Author profiling, Instant messages, Machine learning, Digital footprints

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https://www.sciencedirect.com/science/article/pii/S0747563221003137

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The processing of emoji-word substitutions: A self-paced-reading study

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Highlights

• Sentence comprehension does not suffer when emojis replace words.

• Emojis within sentences are processed like pictures.

• In special circumstances, emojis can activate an entire lexical entry, including phonological information.

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Abstract

In computer-mediated communication, emojis can be used for various purposes. 

As small graphical images, many emojis depict abstract or concrete objects ideogrammatically. 

We report on a self-paced reading experiment of sentences containing emojis. 

We tested to what extent emojis encode lexical meanings when used in a sentence context. 

First, we confirm earlier findings that sentence comprehension does not suffer when emojis replace words. 

Second, we show that in addition to the graphically encoded concept, emojis in some cases enable the retrieval of an entire lexical entry, including the phonological value of the associated word. 

This means that even emojis showing a homophonous noun to the target word, such as “palm (tree)” for “palm (of hand)” can be interpreted correctly in context. 

Based on measured differences in the reading times between words, emojis depicting the intended target referent, and emojis depicting a homophonous noun, we propose a context dependent account of emoji interpretation.

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Keywords

Emojis, Self-paced reading, Lexical ambiguity, Homonymy, Processing

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https://www.sciencedirect.com/science/article/pii/S074756322100399X

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Development and validation of the ‘Lebender emoticon PANAVA’ scale (LE-PANAVA)

Development and validation of the ‘Lebender emoticon PANAVA’ scale (LE-PANAVA) for digitally measuring positive and negative activation, and valence via emoticons

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Highlights

• ‘Experience Sampling Method’ (ESM) requires short and validated non-verbal scales.

• The non-verbal ‘Lebender Emoticon PANAVA’ scale (LE-PANAVA) is presented.

• LE-PANAVA captures positive and negative activation (PA/NA), and valence (VA).

• LE-PANAVA is available for future ESM research and practical application.

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Abstract

Positive and negative activation (PA/NA) represent two general activation systems of affect that are of importance for studying personality. 

Hereby, many studies focus on state assessment of PA and NA in everyday situations, using the ‘Experience Sampling Method’ (ESM) performed via mobile devices. 

ESM studies require short, reliable and validated non-verbal scales for immediate and fast capturing of personality and situation characteristics. 

In this study we present the non-verbal ‘Lebender Emoticon PANAVA’ scale (LE-PANAVA), consisting of five items capturing PA, NA, and valence (VA). 

LE-PANAVA is based on the 10-item verbal PANAVA-KS scale developed by Schallberger (2005). 

The development of LE-PANAVA consisted of a three step process: The graphical development and selection of a set of emoticons (study 1), the validation of the set of emoticons and corresponding adjustments to the scale (study 2), and validation of the final scale (study 3). 

We conclude from the results that LE-PANAVA captures the two factors PA and NA, but are aware that they are closely interrelated. 

Additional to LE-PANAVA, an ultra-short version was derived, that is, a forced choice 2 × 2 matrix of emoticons – the ‘Lebender Emoticon PANA Matrix’ (LE-PANA-M). Both LE-PANAVA and LE-PANA-M are available for future research and practical application.

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Keywords

Positive activation, Negative activation, Valence, PANAVA, Emoticon, Experience sampling method (ESM)

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https://www.sciencedirect.com/science/article/pii/S0191886920301124

 

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A comparison of five methodological variants of emoji questionnaires for measuring product elicited emotional associations

A comparison of five methodological variants of emoji questionnaires for measuring product elicited emotional associations: An application with seafood among Chinese consumers

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Highlights

• Emoji were used to measure emotional associations to seafood product names.

• Emoji profiles for mussels, lobster, squid and abalone differed among Chinese consumers.

• Emoji product profiles did not largely vary with question wording.

• Higher emoji citation frequency were found with forced Yes/No and RATA than CATA questions.

• RATA improved discrimination among the product stimuli compared to CATA.

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Abstract

Product insights beyond hedonic responses are increasingly sought and include emotional associations. 

Various word-based questionnaires for direct measurement exist and an emoji variant was recently proposed. 

Herein, emotion words are replaced with emoji conveying a range of emotions. 

Further assessment of emoji questionnaires is needed to establish their relevance in food-related consumer research. 

Methodological research contributes hereto and in the present research the effects of question wording and response format are considered. 

Specifically, a web study was conducted with Chinese consumers (n = 750) using four seafood names as stimuli (mussels, lobster, squid and abalone). 

Emotional associations were elicited using 33 facial emoji. 

Explicit reference to “how would you feel?” in the question wording changed product emoji profiles minimally. 

Consumers selected only a few emoji per stimulus when using CATA (check-all-that-apply) questions, and layout of the CATA question had only a small impact on responses. 

A comparison of CATA questions with forced yes/no questions and RATA (rate-all-that-apply) questions revealed an increase in frequency of emoji use for yes/no questions, but not a corresponding improvement in sample discrimination. 

For the stimuli in this research, which elicited similar emotional associations, RATA was probably the best methodological choice, with 8.5 emoji being used per stimulus, on average, and increased sample discrimination relative to CATA (12% vs. 6–8%). 

The research provided additional support for the potential of emoji surveys as a method for measurement of emotional associations to foods and beverages and began contributing to development of guidelines for implementation.

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Keywords

Emotion measurement, Emoticons, Research methods, Consumers, ChinaSeafood

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https://www.sciencedirect.com/science/article/abs/pii/S0963996917301898

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An empirical study of emoji usage on Twitter in linguistic and national contexts

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Abstract

Emojis or ‘picture characters’ have become ubiquitous in modern-day digital communication, including social media sharing and smartphone texting. 

Despite this ubiquity, many questions remain about their usage, especially with respect to global variations in language and country. 

These questions are important, in part because they reveal how people communicate digitally on social platforms, but also because they provide a lens through which different regions and cultures can be studied. 

In this paper, we conduct a principled, quantitative study to understand emoji usage in terms of linguistic and country correlates. 

Our study involves 30 languages and countries each, and is conducted over tens of millions of tweets collected from the Twitter decahose over an entire month. 

Drawing on both statistical measures and information theory, our results reveal that, not only does emoji usage have strong dependencies at both the language and country level, but that some languages and countries are much more constrained in the diversity of their emoji usage. 

However, we also discover that the ‘popularity’ of emojis, both globally and within the context of a given language, follows a robust and invariant trend that emerges fairly quickly (over just a day’s worth of data) and cannot be explained either by a power-law or Heap’s law-like distribution.

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Keywords

Twitter, Emojis, Empirical study, Linguistic dependencies, National dependencies

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https://www.sciencedirect.com/science/article/pii/S2468696421000318 

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Should emoji replace emotion words in questionnaire-based food-related consumer research?

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Highlights

• Invited opinion paper on emoji questionnaires in food-related emotion research.

• Emoji have the potential to be a strong alternative to emotion word questionnaires.

• Overview of concerns that hinder uptake of emoji questionnaires in applied research.

• Suggestions for accelerating emoji questionnaire uptake in sensory and consumer science.

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Abstract

Emotion word questionnaires remain dominant in food-related emotion research despite acknowledged limitations. 

Emoji questionnaires can overcome some of these limitations but are not yet regarded as a strong alternative to emotion word questionnaires. 

We believe they have the potential to earn this right and use this invited opinion piece to explain why. 

Following a brief introduction about emoji, advantages of emoji questionnaires are summarised: – enhanced ecological validity, familiarity and cross-culturally shared meanings. 

We then address concerns that hinder uptake of emoji questionnaires, including emoji meanings/interpretations, appropriateness for older consumers and ability to represent emotional arousal (activation to deactivation). 

Finally, we offer suggestions for how to accelerate the uptake of emoji questionnaires in sensory and consumer science, which include: i) more applied studies using emoji questionnaires, ii) establishing emoji meanings in the context of eating of drinking, iii) adopting open-ended question formats, iv) comparing emoji and emotion word questionnaires, and v) establishing guidelines for emoji questionnaires. 

An important spill-over benefit of research to develop emoji questionnaires is that it exposes the lack of similar research for emotion word questionnaires. 

We encourage colleagues in the field to engage in the task of conducting needed methodological research on emoji and emotion words alike.

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Keywords

Research methods, Emotion research, Measurement, Eating and drinking, Consumers, Product research

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https://www.sciencedirect.com/science/article/abs/pii/S0950329320303906

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Measurement of product emotions using emoji surveys: Case studies with tasted foods and beverages

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Highlights

• Emoji surveys were used successfully to elucidate product-emotion associations to tasted samples.

• Consumers in Korea and New Zealand applied the method to a wide range of tasted foods and beverages.

• Emoji profiles primarily discriminated between samples based on emotional valence.

• Consumers regarded emoji surveys as easy and simple to complete.

• Emotional associations based on emoji and emotion word surveys were not directly comparable.

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Abstract

An increasing number of methods exist for the measurement of product-emotion associations, and the present research implements emoji surveys for this purpose. 

Emoji warrants attention due to their increasing popularity and spontaneous use by consumers in food-related communications. 

Seven case studies were conducted in Korea and New Zealand (118–141 consumers per study), across which a wide range of commercially available foods and beverages were evaluated. 

Product-emotional associations were obtained with CATA questions featuring 33 facial and 6 non-facial emoji. 

The most frequently used emoji had positive valence, and it was by valence that the dominant sample discrimination occurred. 

Samples with high overall liking were associated with positive emoji, whereas negative emoji were used to characterise samples with low overall liking. 

Some evidence was found of samples with the same average overall liking scores being associated with different emoji. 

There was also evidence of sample characterisation and discrimination relative to emotional arousal (high/low), but this was largely secondary to sample discrimination based on emotional valence.

Consumers perceived emoji surveys to be easy and not tedious. 

A comparison of CATA questions containing 25 emoji or 25 emotion words (EsSense25) pointed to similarity in the average number of emoji/emotion words used to characterise samples. 

However, a tendency for word-based responses to be more discriminative and able to determine distinctive emotional profiles for individual samples was found. 

This was attributed to differences in the emotions included in the two survey formats. 

Emoji should not be regarded as a direct substitute for existing word-based emotion surveys, but as an alternative method for emotion measurement. 

Directions for further methodological research to refine emoji surveys are outlined.

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Keywords

Emotion measurement, Emoticons, Research methods, Consumers, CATA questions

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https://www.sciencedirect.com/science/article/abs/pii/S0950329317301350

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Emoji questionnaires can be used with a range of population segments: Findings relating to age, gender and frequency of emoji/emoticon use

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Highlights

• Emoji interpretation was not influenced by gender or frequency of emoji/emoticon use.

• A few age-related differences existed with regard to emoji interpretation.

• Consumers of different gender and age completed emoji questionnaires similarly.

• Emoji questionnaires can be used by more and less frequent users of emoji/emoticon.

• Emoji questionnaires seem suitable for a range of population segments.

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Abstract

The assessment of emoji questionnaires as a method in food-related consumer research is furthered by this methodological study aimed at exploring the extent to which they can be used with a range of population segments. 

In the first part of the paper, a web-based survey was implemented to assess differences in the interpretation of 33 facial emoji using a check-all-that-apply (CATA) question. 

Results showed that while emoji interpretation was not influenced by age and frequency of emoji/emoticon use in computer-mediated communications, age-related differences existed for a few emoji. 

In the second part of the paper, differences in the completion of emoji questionnaires used to measure product-elicited emotional associations were assessed across four studies involving the evaluation of written stimuli and tasted food samples. 

Gender and age did not influence consumer ability to describe and discriminate between stimuli, eliciting emoji profiles that were highly similar. 

Among more frequent users of emoji/emoticon, the average number of emoji used to characterise the stimuli was significant higher than among less frequent users, and there was a tendency toward greater discrimination, but the differences were small and of little concern regarding ability of the less frequent emoji/emoticon users’ ability to perform the research task. 

The findings of this research provide preliminary evidence about the suitability of emoji surveys to measure product-related emotional associations with different consumer populations.

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Keywords

Emotion measurement, Research methods, Consumers

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https://www.sciencedirect.com/science/article/abs/pii/S0950329317303026

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What Is Text Embeddings?

 

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Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts. 

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Embeddings are useful for working with natural language and code, because they can be readily consumed and compared by other machine learning models and algorithms like clustering or search.

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Embeddings that are numerically similar are also semantically similar. For example, the embedding vector of “canine companions say” will be more similar to the embedding vector of “woof” than that of “meow.”

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Text Similarity Models

Text similarity models provide embeddings that capture the semantic similarity of pieces of text. These models are useful for many tasks including clustering, data visualization, and classification.

To compare the similarity of two pieces of text, use the dot product on the text embeddings. The result is a “similarity score”, sometimes called “cosine similarity,” between –1 and 1, where a higher number means more similarity. In most applications, the embeddings can be pre-computed, and then the dot product comparison is extremely fast to carry out.

One popular use of embeddings is to use them as features in machine learning tasks, such as classification. In machine learning literature, when using a linear classifier, this classification task is called a “linear probe.”

Text Search Models

Text search models provide embeddings that enable large-scale search tasks, like finding a relevant document among a collection of documents given a text query. Embedding for the documents and query are produced separately, and then cosine similarity is used to compare the similarity between the query and each document.

Embedding-based search can generalize better than word overlap techniques used in classical keyword search, because it captures the semantic meaning of text and is less sensitive to exact phrases or words. 

Source:

https://openai.com/blog/introducing-text-and-code-embeddings/

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