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Age and Gender in Language, Emoji, and Emoticon Usage in Instant Messages


Abstract and Figures

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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Age and Gender in Language, Emoji, and Emoticon Usage in Instant Messages
Timo K. Koch1, Peter Romero2, Clemens Stachl3
1Department of Psychology, Psychological Methods and Assessment, Ludwig-Maximilians-Universität
nchen, Leopoldstr. 13, 80802 Munich, Germany,
2Graduate School of Economics, Advanced Cognition Labs, Keio University, 2-15-45 Mita, Minato-ku, Tokyo
108-8345, Japan,
3Institute of Behavioral Science and Technology, University of St. Gallen, Torstrasse 25, CH-9000 St. Gallen,
This manuscript has been accepted for publication in Computers in Human Behavior.
Please cite as:
Koch, T. K., Romero, P., & Stachl, C. (2021). Age and gender in language, emoji, and
emoticon usage in instant messages. Computers in Human Behavior.
Supplementary materials for this manuscript, including code, figures, and results, are
available in the project's repository on the Open Science Framework (OSF):
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
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.
Keywords: Age; Gender; Author profiling; Instant messages; Machine learning; Digital
1. Introduction
When texting a friend on WhatsApp, posting on Facebook, tweeting on Twitter, or
writing a blog post, we inevitably leave behind digital footprints in the form of text data.
Research in the domain of author profiling has shown that language characteristics of
Facebook status updates (Jaidka et al., 2018; Sap et al., 2014; Schwartz et al., 2013), tweets
(Bamman et al., 2014; Burger et al., 2011; Jaidka et al., 2018; Rao et al., 2010; Sap et al.,
2014), and blog posts (Argamon et al., 2007; Sap et al., 2014; Schler et al., 2006) allow for
the accurate inference of the authors’ age and gender. Moreover, these social media studies
extended the theory of gender- and age-linked language variations (Park et al., 2016). Instant
messaging services (e.g., WhatsApp, Facebook Messenger, WeChat) also produce vast
amounts of digital footprints every day, but have rarely been investigated in language studies.
Unlike data from social media platforms, like Facebook, Twitter, and Reddit, text from
instant messaging is not easily accessible to researchers through an application programming
interface (API). However, for technology companies and governments, data from private
instant messaging offer an emerging opportunity for user profiling and targeting that seems to
increasingly move into their focus (Evans, 2020; Goodin, 2021). In a similar manner to prior
studies based on social media posts, this work aims to create insights into age- and gender-
linked linguistic variations and explore how accurately information on user demographics can
be inferred from instant messages.
1.1. Linguistic variations with age and gender
Prior studies on a variety of text sources, such as writing samples, speech transcripts,
exams, or collected works of well-known writers, have investigated the association of
linguistic style with age and gender in a descriptive nature (Newman et al., 2008; Pennebaker
& King, 1999; Pennebaker & Stone, 2003). Findings from these studies indicate that
women’s language is centered around discussing people and their activities. Furthermore,
women were found to use more words related to psychological processes, such as emotions
(e.g., “anxious”), and social processes, for example “talk”. Men’s language has been
identified to be rather focused on the description of external events, objects, and processes.
For example, men were found to use words related to occupation (e.g., “job”), swear words,
and numbers more often than women do (Newman et al., 2008).
Regarding linguistic differences with age, research suggests that older people use
more positive emotion words (e.g., “happy”), fewer negative emotion words, like “angry”,
and fewer self-references, such as “me”. Past findings also suggest that the use of future-
tense increases with older age, whereas the use of past-tense decreases and people
demonstrate a general pattern of increasing cognitive complexity (Pennebaker & Stone,
2003). With the advent of computer-mediated communication (CMC), descriptive research
on linguistic variations with respect to demographic differences has shifted to digital data
sources, such as blogs (Argamon et al., 2007) and social media posts (Park et al., 2016), but
has not yet included instant messaging data to our knowledge.
In contrast to traditional text, like books or letters, digital text is often enhanced with
graphical symbols, such as emoticons and emoji. These characters are used to augment the
text with additional information. Particularly in CMC, like instant messaging, emoticons and
emoji play a central role. Emoticons, for example “;-)”, represent facial expressions and can
enrich messages with emotional context. Emoji are graphical symbols that allow giving
meaning to a message, for example by adding contextual cues (“Do you want to hang out
tonight? !”) or replacing words (“Is there still " left?”) beyond the expression of emotions
(“How could you do that to me? #”; Bai et al., 2019; Völkel et al., 2019). 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
Findings on the associations of age with the usage of emoticons and emoji are
diverse: Whereas an analysis of Facebook status updates suggested that younger users post
more emoticons than older users do (Oleszkiewicz et al., 2017), other studies on online chat
rooms (Fullwood et al., 2013) and WhatsApp messages (Pérez-Sabater, 2019) did not find
significant age differences in emoticon usage. 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. Findings from studies based on
Facebook status updates (Oleszkiewicz et al., 2017), online chat rooms (Fullwood et al.,
2013; Wolf, 2000), SMS (Tossell et al., 2012), and WhatsApp messages (Pérez-Sabater,
2019) suggested that women use more emoticons than men. Tossell and colleagues (2012)
also found that men used a more diverse range of emoticons in their SMS data than women.
The observed gender differences seem to exist for emoji, too: A large-scale study on
smartphone users provided evidence that women use more emoji in their communication than
men (Chen et al., 2018), contradicting a smaller study on Chinese WeChat users suggesting
that gender has no effect on emoji usage (An et al., 2018). Also, women reported to use emoji
(but not emoticons) more often than men in studies with self-reported survey data (Jones et
al., 2020; Prada et al., 2018).
1.2. Predicting age and gender from social media posts & transfer to instant messages
Recent research on age- and gender linked language variations has extended the
existing descriptive work with a prediction-oriented approach. Hereby, novel machine
learning methods trained on social media text data have been deployed to infer demographic
characteristics of individuals or communities (Kern et al., 2016). Machine learning
algorithms can be used to detect generalizable predictive patterns in rich text data sets on a
large number of language features and to associate these with demographics. Using this
approach, researchers were able to make inferences of users’ age and gender based on
language features extracted from Facebook status updates (Jaidka et al., 2018; Schwartz et
al., 2013), tweets (Burger et al., 2011; Jaidka et al., 2018; Marquardt et al., 2014; D. Nguyen
et al., 2013; T. Nguyen et al., 2011; Rao et al., 2010), and blog posts (Argamon et al., 2007;
Marquardt et al., 2014; D. Nguyen et al., 2011; Schler et al., 2006). These studies created
unprecedented insights into the associations of individual differences and language usage. By
exposing how much personal information can be inferred from digital footprints on social
media, this body of research also started a societal discussion about the necessity to protect
individual privacy on social media.
Findings from demographic prediction studies on social media posts might not
necessarily generalize to instant messages due to each channel’s specific language
peculiarities. For example, language usage in a given channel is also affected by its technical
affordances. For instance, tweets are limited to 280 characters. Moreover, it could be shaped
by the respective audience and goals of use: While private instant messaging is used to
communicate with selected chat partners, social media allows to reach out to a larger
readership to, for example, transmit information on one’s general activities (Quan-Haase &
Young, 2010). As a consequence, users engage in varying levels of self-disclosure between
private instant messages and social media posts as well as across social media platforms
(Bazarova & Choi, 2014; Jaidka et al., 2018). Therefore, the same user can exhibit different
linguistic styles across channels (Bazarova et al., 2013; Jaidka et al., 2018). For example,
prior research indicates that users prefer to self-disclose more on Facebook than on Twitter,
which could be one reason
why language models trained on Facebook posts are more
accurate at predicting users’ age and gender than those trained on Twitter posts (Jaidka et al.,
2018). Based on findings suggesting that users engage in more self-disclosure in private
instant messages compared to social media posts (Bazarova & Choi, 2014) and reports that
higher levels of self-disclosure lead to more accurate predictions of demographics (Jaidka et
al., 2018), instant text messages could be more predictive of user characteristics than social
media posts. As a consequence, new machine learning models trained on instant messaging
data are needed in order to determine their predictiveness for user characteristics, such as
demographics. However, only a limited number of small chat corpora are available to the
research community yet that would allow for such predictive work (Verheijen & Stoop,
In conclusion, past findings on age- and gender-specific language variations and the
successful prediction of user demographics from social media posts (e.g., Jaidka et al., 2018;
Schwartz et al., 2013) motivate us to address the gap in author profiling research based on
instant messages. In this work, we systematically investigate age- and gender-linked language
variations in WhatsApp messages. Specifically, we replicate established methods of closed
and open vocabulary approaches from existing social media research and extend our analyses
Twitter's character limit could be another.
to include features specific to instant messages (i.e., general message characteristics and
emoji preferences). Additionally, we investigate if user demographics can be predicted from
these differences in linguistic characteristics using machine learning algorithms. For this
purpose, we present and apply methodological approaches to predict user characteristics even
from comparably small and imbalanced text data sets. In our models, we also identify the
most predictive age- and gender-related language features. Finally, we discuss implications of
our findings for psycholinguistic theory and user privacy in instant messaging.
2. Method
2.1. Data set
The “What’s up, Deutschland?” chat corpus was collected by Siebenhaar and
colleagues (2018) in Germany from November 2014 until January 2015. German WhatsApp
users were invited to donate a chat conversation by exporting a WhatsApp chat of their
choice as a plain text file and to email it to the researchers. Media files, like pictures or
videos, were not included in the corpus due to copyright and unresolved privacy implications.
We counted the placeholders from media files for quantitative analysis. Upon receipt of a
chat log, an informed consent form was sent to all chat partners, stating that their text may be
used and cited anonymously for scientific purposes. If the signed consent form was not
returned until 14 days after the end of the data collection, the contents of all messages of the
respective users were replaced by anonymous placeholders. The data of the consenting
volunteers were manually anonymized: Addresses, last names, telephone numbers, location
notifications, and bank account details were replaced by categorical placeholders (e.g.,
“Tobias” by “NAME_M” indicating a male first name). While the “What’s up,
Deutschland?” chat corpus is not yet available publicly, the authors kindly provided us with
early access to the data.
The original corpus contains data from 495 consenting volunteers, who sent 451,938
messages in 218 chats. We excluded 260 volunteers, who did not provide demographic
information on age and gender. Additionally, we removed data from nine volunteers with less
than 50 words of text data, because this is the recommended minimum to obtain meaningful
results from the LIWC software we used to extract linguistic features (Receptiviti, 2019). The
final dataset included 162 women and 64 men of an average age of 27.08 years (SD = 10.03),
with no substantial age difference between men and women. The 226 volunteers contributed
a total of 309,229 WhatsApp messages containing 1,968,349 words, 81,199 emoji, and
48,814 emoticons. The average volunteer submitted 1,368,27 messages (SD = 3,391.38) with
8,709.51 words (SD = 20,806.01). The volunteers used an average of 32.85 different emoji
(SD = 45.40) and 5.74 different emoticons (SD = 5.91) in their donated messages. For
predictive modeling, we applied a minimum threshold of 1000 words that had been used in
comparable prior work on social media posts (Schwartz et al., 2013), resulting in a sample of
157 volunteers.
2.2. Language analyses
Users convey information in instant messages through a variety of means, like text,
emoji and emoticons, audio files, images, or videos. Therefore, we extracted five sets of
features to comprehensively quantify the characteristics of volunteers’ donated WhatsApp
messages (see Table 1). First, we quantified messages’ text attributes through a theory-driven
dictionary (LIWC), words and phrases (n-grams), and topics. This procedure represents a
standard approach in language analyses of social media posts (Eichstaedt et al., in press; Kern
et al., 2016). Second, we computed features quantifying general message characteristics and
emoji preferences to capture additional information from instant messages. We estimated the
size of gender differences for all features using Cohen's d effect sizes and the magnitude of
the age association using pairwise Pearson correlation. We only considered coefficients
where the 95% confidence interval did not contain zero.
Table 1
Computed features for the age- and gender-linked language analyses
Feature type
Number of
Words and phrases
General message
Emoji preferences
Note. List of computed features from volunteers’ WhatsApp messages for the age- and gender-linked language
2.2.1. Closed and open vocabulary analysis
In this work, we replicated the methods used in prior research on social media posts
that made use of a combination of closed vocabulary and open vocabulary approaches in
order to predict user demographics (for a detailed description of methods see Jaidka et al.,
2018; Schwartz et al., 2013). Closed vocabulary methods follow a top down approach in form
of a-priori defined dictionaries, whereas in open vocabulary methods the features are created
bottom up from the data. Open vocabulary feature extraction methods routinely show
superior predictive power over closed vocabulary approaches (Eichstaedt et al., in press).
We used the well-established Linguistic Inquiry and Word Count (LIWC) text
analysis program (Pennebaker, Booth, et al., 2015) with the latest German dictionary (Meier
et al., 2019). LIWC has a predefined dictionary that features words and word stems, which
have been categorized in theory-derived linguistic dimensions, such as standard language
categories (e.g., pronouns) or psychological processes (e.g., positive and negative emotion
words). LIWC counts the words in the respective word categories and computes a score for
each category to indicate the relative prevalence of the words from each category in the given
text. Since the word categories in LIWC are identical across languages, we can compare the
scores for the word categories from our German text data with other studies based on text
data in English.
Due to the absence of pre-trained topic models and age-/gender-linked lexica
available for German instant messages or social media posts, as these exist for English (Sap
et al., 2014; Schwartz et al., 2017), and since models are not readily transferrable across
platforms and languages (Jaidka et al., 2018), we created data-driven features based on our
chat corpus. Therefore, we tokenized volunteers’ messages using an emoticon-aware
tokenizer into single words. Further, we grouped the tokens into sequences of two to three
words termed phrases. We kept phrases with a PMI (pointwise mutual information)
than 2*length, length being the number of words contained in the respective phrase.
Moreover, we kept words and phrases that were used by at least 5% of volunteers to keep the
focus on common language. All word and phrase counts were normalized by each volunteer’s
total use of words and phrases, respectively. Similar to Schwartz et al., (2013), we extracted
2,000 topics by using Latent Dirichlet Allocation (LDA) with Gibbs sampling (alpha = 0.30).
The LDA’s underlying assumption is that documents (in our case WhatsApp messages) are a
PMI quantifies the probability of the co-occurrence of words (Church & Hanks, 1990).
probability distribution over topics, and that topics are a probability distribution over words.
In this manner, each topic is represented as a set of words with their respective probabilities.
For example, one extracted topic contains the words “work”, “tired”, and the “-.-” emoticon,
which may indicate that the sender is annoyed by work. To use topics as features, we
computed the probability of a volunteer mentioning each of the 2,000 topics by summing up
the product of the normalized word use from that volunteer and the topic probability of the
given word from the LDA.
2.2.2. General message characteristics
We extracted a range of features describing the general properties of volunteers’
messages related to the included text, media files, contacts, emoji, and emoticons. Here, we
calculated the average number of words per message, the share of messages, which contained
a media file (e.g., audio, video, or image), and the share of messages containing a contact
card. Further, we computed metrics on the use of emoji and emoticons. We calculated the
share of messages containing any emoji or emoticon, only emoji and emoticons, the
volunteers’ average number of emoji and emoticons per message, and the emoji- and
emoticon-to-word ratios for each volunteer. To investigate the individual range of emoji and
emoticon use, we counted the number of unique emoji and emoticons used by each volunteer
across all messages. We then divided this number by the total number of emoji (694) and
emoticons (68) used by all volunteers in the entire corpus to express the individual ratio. In
the same manner, we calculated the average range of emoji/ emoticon use per message for
each volunteer by dividing the number of unique emoji and emoticons per message by the
total number of unique emoji and emoticons used in all messages from this volunteer.
Thereafter, we divided the respective fractions by the number of messages from each
volunteer. For example, if a volunteer had used 10 different emoticons in 75 messages, the
relative emoticon range per message in relation to all emoticons used in the corpus was
(10/68)/75 = 0.002.
2.2.3. Emoji preferences
In the same manner as the frequencies for words and phrases, we considered all
specific emoji (179) that had been used by at least 5% of volunteers. We then counted how
often each volunteer had used the respective emoji and normalized their frequency use by
dividing the count by the total number of emoji used by the respective volunteer.
2.3. Predicting demographics
For the prediction of volunteers’ age and gender from instant messages, we trained
multiple supervised machine learning algorithms on the extracted features while accounting
for the comparably small size and gender-imbalance of our data set. We compared the
predictive performance of an Elastic Net (Zou & Hastie, 2005), a non-linear tree-based
Random Forest (Breiman, 2001; Wright & Ziegler, 2017), and a baseline model. For the
prediction of age, the baseline model would predict the mean age in the respective training
set for all cases in a test set. For gender classification, it would always predict the more
frequent class (in our case women) in the respective training set for all cases in a test set. We
chose these particular algorithms because they allowed us to capture linear predictor effects
in the data with Elastic Net models as well as non-linear effects with Random Forest models.
Further, they are widely adopted in research exploring social media text using machine
learning methods (Jaidka et al., 2018).
2.3.1. Model fitting on small and imbalanced data
Due to the comparably small size of our data set we evaluated the predictive
performance of our models and tuned model hyperparameter in a nested cross-validation
scheme (Bischl et al., 2012). In this approach, the respective model’s hyperparameters are
optimized across five folds in an inner cross-validation loop, using a random search approach
with ten iterations. In an outer cross-validation loop, the overall model performance with the
tuned hyperparameters is evaluated across twenty folds with five repetitions. This procedure
prevents an overestimation of the model’s predictive performance due to model overfitting
when finding optimal hyperparameters and evaluating predictive performance. For the gender
classification models, the cross-validation folds were stratified in the resampling to ensure
that the ratio of men to women was the same across each fold and equal to the full dataset.
For Random Forest models, we tuned the hyperparameter for the number of variables
available for splitting at each tree node and pragmatically set the number of trees to 1000 as a
computationally feasible large number (Probst & Boulesteix, 2017). In Elastic Net models,
we tuned the regularization parameter lambda and the mixing parameter alpha. Additionally,
for gender classification, we used automatic tuning of the threshold values (in our case the
probability value above that threshold indicates "woman", a value below indicates "man") for
all algorithms. In each fold of both the inner and outer cross-validation loops, constant
variables (i.e., less than 2% variance) were dropped in the process. Next, we had to further
reduce the number of features since there were still way more features than volunteers.
Therefore, the 1000 features with the highest Spearman rank correlation (for the age
prediction) and the highest F-Values from a Kruskal-Wallis test (for the gender prediction) in
a respective training fold were retained for predictions on the test data.
2.3.2. Model evaluation
We evaluated the predictive performance of the age regression models based on the
mean absolute error (MAE), Pearson correlation (r) between the predicted age and volunteers’
self-reported age, and the coefficient of determination (R2). In the gender classification task,
we had to account for the small and imbalanced classes. In such imbalanced classification
settings, it is important to consider class-specific performance metrics in addition to overall
metrics. We report the prediction accuracy, F-Score (F1)
, and area under the curve (AUC)
for overall performance. Further, we supply sensitivity (true positive rate; in our case
correctly classified men) and specificity (true negative rate; in our case correctly classified
women) to evaluate the prediction performance for both gender classes. We computed
performance measures within each fold of the outer cross-validation procedure and calculated
the median across all folds within each prediction model. We chose the median since it is less
affected by outliers in the predictions across folds than the mean. To determine whether a
model was predictive (α = 0.05) at all, we used variance-corrected t-tests to compare the
performance measures in all prediction models with those from the baseline models. These
variance corrected t-tests accounted for the dependence structure of cross-validation
experiments (Nadeau & Bengio, 2003). All p-values were adjusted for multiple comparisons
(n = 12) via Holm correction.
2.3.3. Model interpretation
Because flexible machine learning models cannot be interpreted in a straight forward
manner, they are sometimes referred to as “black boxes” (Yarkoni & Westfall, 2017). We
used interpretable machine learning methods to increase the interpretability of our predictive
models and derive relevant information for psycholinguistic theory. In order to quantify the
impact of predictors in a Random Forest model trained on all predictive feature sets, we
computed out-of-bag (OOB) permutation variable importance (Breiman, 2001; Wright et al.,
. For Elastic Net models, we inspected the standardized regularized regression weights
The F-Score represents the harmonic mean of a model’s precision and recall performance in one
metric and ranges between 0 and 1.
The AUC describes the area under the receiver operating characteristics curve when plotting the true
positive rate on the y-axis against the false positive rate on the x-axis onto a two-dimensional space. AUC can
range between 0 and 1, where 0 indicates the worst separability, and 1 represents a perfect separation of the
OOB permutation variable importance is determined by shuffling (permuting) values in the variables
and by evaluating the model’s prediction performance in the data that is not used for tree fitting (Wright et al.,
to detect import variables in the predictions. Further, we created accumulated local effect
(ALE) plots to visualize the effects of individual predictor variables on the predictions in
Random Forest models (Apley & Zhu, 2020). The depicted values in the ALE plots represent
the mean change in predicted criterion values compared to the model’s average prediction,
for the given value-ranges of a predictor variable (Molnar, 2019).
2.4. Software & open materials
All data processing and statistical analyses in this work were performed with the
statistical software R version 4.0.2 (R Core Team, 2020). For text processing, we used the
quanteda (Benoit et al., 2018), udpipe (Straka & Straková, 2017), and tm (Feinerer et al.,
2008) R packages. We extracted LDA topics using the topicmodels (Grün & Hornik, 2011) R
package. For machine learning, we used the mlr framework (Bischl et al., 2016), including
the mlrCPO (Binder et al., 2020) package for pre-processing. Further, we used the glmnet
(Friedman et al., 2010) and ranger (Wright & Ziegler, 2017) packages to fit prediction
models. Moreover, we created ALE plots with the iml package (Molnar, 2018). To make our
work transparent, we provide the R code, our main figures, and results in the project’s
repository on the Open Science Framework ( We pre-registered our
analyses before accessing the data. The pre-registration protocol and a document describing
the deviations from the pre-registration protocol are provided in the repository.
2016). Permuting the values of unimportant variables should not affect the prediction performance, but
permuting important variables should.
3. Results
3.1. Age- and gender-linked variations
We found a range of age- and gender-linked language variations in our data. A
comprehensive overview is provided in the repository. When interpreting and generalizing
results, particularly of the data-driven open vocabulary features, such as words and phrases,
one should keep in mind the small size of and the gender-imbalance in the data set.
3.1.1. Closed and open vocabulary analysis
Table 2 lists the top ten age- and gender-linked variations in LIWC word categories.
Regarding age, words from the informal language category, particularly netspeak (including
emoticons) and fillers, were written more often by younger volunteers. In the same manner,
younger volunteers used first person singular (e.g., “I”), words indicating causation (e.g.,
“because”), and interrogatives (e.g., “why”) more often. On the contrary, future-focused
words (e.g., “tomorrow”) and words from the family category (e.g., “children”) were used
more frequently by older volunteers. Moreover, the Clout score, which indicates high
expertise, confidence, and future orientation was higher for older volunteers. Finally, older
volunteers used more periods in their messages, which is closely related to the negative
correlation of words per sentence with age since fewer periods suggest longer sentences to
LIWC. In line with LIWC results, words and phrases indicative for informal langue, for
example “ne” which translates to “a/one/no” (r = -0.42), “haha” (r = -0.26), and “geil” (engl.
“hot/great”; r = -0.26) were used more often by younger volunteers. In the same manner,
younger volunteers included more emoticons, such as “:)” (r = -0.32) and “:D” (r = -0.31), in
their messages. On the contrary, older volunteers used words and phrases revolving around
salutations, for example “greetings” (r = 0.33) or “good morning”, r = 0.29), more frequently.
Further, older volunteers used words related to work, such as “office”; r = 0.25), more often.
Topics were not as age-discriminative and clearly interpretable as words and phrases. Hence,
we refrain from interpreting these effects.
On average, women used more function words, particularly personal pronouns in first
person singular, such as “I”, and conjunctions (e.g., “and”) than men. LIWC recognized more
words from women’s messages and women used more exclamation marks than men.
Furthermore, women incorporated more words referring to insights (e.g., “looking forward
to”) and home, for example “family”. On the contrary, men scored higher on the summary
language variable Analytic Thinking, which indicates a rather formal, logical, and hierarchical
thinking style in contrast to an informal, personal, here-and-now, and narrative thinking
(Pennebaker, Boyd, et al., 2015). Female volunteers used various forms of the verb “to go”(d
= 0.51) more often. Men used more abbreviations, colloquial language, and words related to
alcohol consumption, like “beer” (d = -0.50), and sex. Similar to age, gender-discriminative
topics were not as clearly interpretable as words and phrases. The only distinctive female
topics (d = 0.34) revolved around social activities, containing words such as “meeting”,
“seeing”, and “drinking”. The most distinctive male topic (d = -0.43) could be interpreted as
salutations, containing, for example, “hey”, “hi”, and “xD”.
Table 2
Top ten language variations in LIWC categories with volunteer age and gender
r CI 95%
Informal language
[-0.53, -0.31]
Focus future
[0.27, 0.49]
(Informal language/)Netspeak
[-0.49, -0.26]
[0.26, 0.48]
(Total function words/ Total pronouns / Personal pronouns/) 1st person singular
[-0.47, -0.24]
(Informal language/) Fillers
[-0.44, -0.21]
(Cognitive processes/) Causation
[-0.44, -0.21]
(Social processes/) Family
[0.18, 0.42]
(Total punctuation/) Period
[0.16, 0.40]
[-0.40, -0.15]
M (Men)
M (Women)
d CI 95%
Total function words
[0.37, 0.96]
Analytic thinking
[-0.95, -0.36]
Dictionary words
[0.31, 0.90]
(Total function words / Total pronouns/) Personal pronouns
[0.29, 0.89]
(Total function words/) Total pronouns
[0.19, 0.78]
(Total function words / Total pronouns / Personal pronouns/) 1st person singular
[0.19, 0.78]
(Total function words/) Conjunctions
[0.12, 0.70]
(Total punctuation/) Exclamation marks
[0.10, 0.69]
(Cognitive processes/) Insight
[0.09, 0.67]
[0.08, 0.66]
Note. N = 226. Table rows are ordered by absolute magnitude of the Pearson correlation coefficient for age and absolute magnitude of effect size for gender. Women are
coded “1” and men are coded “0”. For linguistic characteristics, the hierarchically superior LIWC categories are in parentheses. For example, the notion “(Cognitive
processes/) Insight” indicates that “Insight” is a subcategory of “Cognitive processes”.
3.1.2. General message characteristics
We found the usage of emoticons to be closely associated with volunteer age.
Specifically, older volunteers used emoticons less frequently and in a less diverse manner.
This finding is reflected in the negative correlations of emoticon-to-word ratio (r = -0.45), the
share of messages containing at least one emoticon (r = -0.40), the average number of
emoticons per message (r = -0.37), share of messages containing only emoticons (r = -0.19),
and the use of unique emoticons used from the entire corpus (r = -0.18) with age. While the
frequency of emoji usage was not correlated with age, the range of emoji usage was: Older
volunteers used a broader range of unique emoji overall (r = 0.17) and incorporated more of
their own unique emoji (r = 0.15) in a message. Finally, older volunteers sent longer
(containing more words) messages (r = 0.20) and more media files (r = 0.19).
With regard to volunteer gender, we found that women used emoji more frequently
and in a more diverse manner. Specifically, women had a higher average number of emoji per
message (d = 0.54), share of messages containing at least one emoji (d = 0.53), emoji-to-
word ratio (d = 0.43), and used a broader share of unique emoji from entire corpus (d = 0.41)
than men. For emoticons, there were no gender differences present in the data.
3.1.3. Emoji preferences
Emoji preferences varied with volunteer age. We found emoji expressing emotions,
for example “!” (r = -0.17), “"” (r = -0.17), “#” (r = -0.17), “$” (r = -0.17), and “%” (r =
-0.16), to be more frequently used by younger volunteers in our data set. On the contrary, we
found emoji depicting objects and people, for example “&” (r = 0.20), “'” (r = 0.19), “(
(r = 0.18), “)” (r = 0.17), and “*” (r = 0.17), to be more frequently used by older
volunteers. A similar pattern emerged in the emoji preferences across genders: Women
preferred emoji that express positive emotions, for example “+” (d = 0.51) and “,” (d =
0.45). Men, on the other hand, preferred the disappointed emoji “-” (d = -0.30), representing
a negative emotion.
3.2. Predicting demographics
After investigating age- and gender-linked language variations, we used cross-
validated machine learning models to predict volunteers’ demographics and identified
predictive language features. We provide an overview of the performance of all models in the
project’s repository.
3.2.1. Age regression
Random Forest models (MAEMd = 3.95, rMd = 0.81, R2Md = 0.49) and Elastic Net
models (MAEMd = 4.35, rMd = 0.79, R2Md = 0.41) predicted age significantly better than the
baseline model (MAEMd = 6.63, rMd = NA, R2Md = -0.08). Our results suggest that all feature
sets, except topics, were significantly predictive of volunteers’ age above baseline (see Figure
1). Moreover, the findings indicate that the Random Forest algorithm performed on average
better than the Elastic Net algorithm in the age prediction. The large variance in prediction
performance across folds for the three observed different performance measures was likely
caused by the small training and test set size. For R2 there were a few severe negative outliers,
which affected the results of the respective significance tests. For example, while the variance
corrected t-tests based on MAE and r indicated significant above-baseline prediction for
LIWC features and general message characteristics, those t-tests based on R2 did not.
Figure 1. Box and whisker plot of prediction performance measures from 20-fold five times repeated cross-
validation for age regression for each feature (sub) set. The symbol in the boxes represents the median, boxes
include values between the 25 and 75% quantiles, and whiskers extend to the 2.5 and 97.5% quantiles. Whiskers
are not displayed for R2 because of negative outliers. Pearson correlation is not available for the baseline model
because it predicts a constant value, for which correlation measures are not defined. MAE is the mean average
error in years. The figure is available in the project’s OSF repository, under a CC-BY4.0 license.
Figure 2 shows the most important features in the Elastic Net model (based on
standardized regularized regression weights) and the Random Forest model (based on
permutation feature importance) in the age prediction trained on the predictive feature sets
(all features except topics). The corresponding ALE plots for the Random Forest model
indicate the direction of the features’ effect on the age predictions. Regularized regression
weights and permutation feature importance for all predictive feature sets are provided in the
project’s repository. Overall, features related to the frequency of emoticon usage, specifically
the emoticon-to-word ratio, the average number of emoticons per message, and the share of
messages containing at least one emoticon, were most important for the prediction of age in
the Random Forest model
. This finding suggests that, for instance, if volunteers used on
One has to keep in mind that the permutation importance scores of correlated features are ranked
higher in Random Forest models. This does not indicate that they are uniquely more important for the prediction
of an outcome (Strobl et al., 2008).
average less than 0.04 emoticons per message, the model predicted older age. Also, the usage
of specific emoticons, such as “:D”, was highly predictive in the Random Forest model,
suggesting that higher usage frequencies predicted younger age. Finally, the usage of the
word “good” that had often been used in salutations, such as “good morning, and the use of
informal language were important for the Random Forest predictions. For example, if more
than 8% of a volunteer’s words were informal language, the model predicted younger age. In
the Elastic Net model, the usage of words and phrases, for example “office” and “flowers”,
was most important.
Figure 2. Top left: Permutation feature importance for the most predictive features in the Random Forest model
for age prediction. Permutation feature importance represents the decrease in the model’s prediction
performance (MAE) after permuting a single variable. Top right: Standardized regularized regression weights
for the most predictive features in the Elastic Net model for age prediction. Bottom: ALE plots indicate how
mean age predictions in the Random Forest model changed with regard to different values in local value-areas
of the respective predictor variable. For example, the average age prediction decreases with an increasing
emoticon-to-word ratio. ALE values are centred around zero. The figure is available in the project’s OSF
repository, under a CC-BY4.0 license.
3.2.2. Gender classification
Random Forest models (AccuracyMd = 75.0, F1Md= 0.5, AUCMd = 0.83) and Elastic
Net models (AccuracyMd = 75.0, F1Md= 0.37, AUCMd = 0.75) predicted volunteers’ gender
significantly better than the baseline model (AccuracyMd = 75.0, F1Md= 0, AUCMd = 0.5)
model. Specifically, our results suggest that LIWC features and words and phrases were
significantly predictive of volunteers’ gender (see Figure 3). Remarkably, models trained on
LIWC features only, particularly Random Forest models (AccuracyMd = 85.7, F1Md = 0.67,
AUCMd = 0.82), had a higher prediction performance than models trained on all features
combined. Consistent to models for age predictions, these findings indicate that the Random
Forest algorithm performed on average better than the Elastic Net algorithm in the gender
prediction. Also, in the same manner to the age models, there was a large variance in
prediction performance across folds because of the small size of training and test sets.
Further, given the class imbalance in our data, the prediction accuracy across folds suggests
that no feature set allowed for above-baseline predictions, while F1 and AUC do so. Due to
the small sample size and class imbalance (Nwomen = 114; Nmen = 43) in our data set, the
models were much more accurate in classifying cases of the majority class (women) correctly
than those of the minority class (men). Moreover, the models misclassified more men as
women than vice versa (see confusion matrices provided in the repository). Therefore, we
additionally applied the Synthetic Minority Over-Sampling Technique (SMOTE) that creates
new synthetic cases for the minority class based on existing volunteers in order to balance out
the training data (Chawla et al., 2002; Fernandez et al., 2018). However, applying SMOTE
did not improve our models’ overall prediction performance over the initial approach without
oversampling (see results in the repository).
Since our results suggest that Random Forest and Elastic Net models trained on LIWC
features and words and phrase significantly predicted volunteer gender, we investigated
features’ variable importance. We refrained from including data-driven words and phrases in
the feature importance analysis since the results would be specific to the small and
imbalanced classes in our data set and most likely not generalize well. We found a similar
pattern for LIWC features as in the descriptive gender-linked language variations: The usage
of function words, particularly the usage of personal pronouns in first person singular, were
most important with higher values leading to a higher probability of the algorithms predicting
a female volunteer. The respective figure displaying the most important features in the Elastic
Net and Random Forest model (with corresponding ALE plots) is supplied in the project’s
Figure 3. Box and whisker plot of prediction performance measures from 20-fold five times repeated cross-
validation for gender classification for each feature (sub) set. The middle symbol represents the median, boxes
include values between the 25 and 75% quantiles, and whiskers extend to the 2.5 and 97.5% quantiles. Outliers
are depicted by single points. For better readability, we omitted the baseline model because F1 is 0 and AUC
(Area under the curve) is 0.5 across all folds (indicated by vertical line). The figure is available in the project’s
OSF repository, under a CC-BY4.0 license.
4. Discussion
Our study has generated novel insights into age- and gender-linked language
variations in open and closed vocabulary features, general message characteristics, and emoji
preferences in instant messages. We predicted volunteer age and gender above baseline levels
and identified particularly predictive features for volunteer demographics in the respective
machine learning models. Finally, we presented methodological approaches to make
predictions from small and imbalanced text data sets.
4.1. Age- and gender-linked language variations in instant messages
We found specific age- and gender-linked language variations to be strongly
associated with volunteer demographics that were also highly predictive. For age-linked
language variations, we found that younger volunteers used emoticons, first person singular,
and informal language more often. Our finding that younger volunteers used emotions more
frequently and that there were no age-related differences in the use of emoji usage are in line
with parts of the previous literature (Oleszkiewicz et al., 2017; Siebenhaar, 2018). However,
our observation that younger volunteers used emoticons more often is not in line with work
by Fullwood et al. (2013), who found no variations in emoticon usage with age among users
in online chat rooms. Our finding that younger volunteers used more words in first person
singular than older ones is in line with results from past studies on a broad range of different
text sources (D. Nguyen et al., 2013; T. Nguyen et al., 2011; Pennebaker & King, 1999;
Schwartz et al., 2013). Pennebaker and Stone (2003) pointed out that this might be an
indicator for people becoming less self-focused as they age. Also, our finding that younger
volunteers used more informal language is in line with past studies that reported similar
effects (T. Nguyen et al., 2011; Schwartz et al., 2013).
Regarding gender-linked language variations, we found that female volunteers used
emoji more often, used a broader range of different emoji, and used more function words -
especially first person singular pronouns. Our observation that women used emoji more often
is in line with prior studies, showing that women on average use more emoji than men (Chen
et al., 2018; Jones et al., 2020; Prada et al., 2018). However, this effect did not generalize to
the usage of emoticons, which were almost equally often used by women and men, in our
data. This finding is in line with work by Tossell et al. (2012), but does not align with results
of Fullwood et al. (2013) and Rao et al. (2010), who found women to use more emoticons in
their data. However, the data for those studies was collected around 2010, when emoticons
were the go-to way to express emotions in computer-mediated communication, before emoji
were around. Over time, the prevalence of emoticons decreased as they were gradually
replaced by emoji (Pavalanathan & Eisenstein, 2015). Since our data set was collected in
2014/2015, many women possibly used emoji instead of emoticons to express emotions,
which could be the reason why gender differences in emoticon usage were not present in our
data. Furthermore, women’s more frequent use of function words and particularly personal
pronouns in first person singular had also been found in previous studies on other text sources
(Newman et al., 2008; Schwartz et al., 2013).
4.2. Predicting demographics from instant messages
Our results in the age predictions based on closed and open vocabulary features
(except topics) on German instant messages compare well with previous results on English
social media data that used the same methods (see Table 3). Our models performed better
than prediction models in prior work by Jaidka and colleagues (2018) trained on tweets and
Facebook posts in predicting user age. Schwartz et al. (2013), who trained their models on an
enormous sample of Facebook posts achieved comparable performances. Many other prior
studies (e.g., Marquardt et al., 2014; T. Nguyen et al., 2011; Rao et al., 2010) binned age into
groups before modeling and, consequently, do not allow for a direct comparison with our
results. For gender predictions, a comparison of our models’ performance with prior work is
not as straightforward because comparable studies only reported their overall classification
accuracy (Bamman et al., 2014; Burger et al., 2011; Schwartz et al., 2013). While this metric
is useful, it is highly dependent on the gender class distribution in the respective sample and
makes a comparison between studies difficult. For all feature sets except LIWC features, we
could not predict gender above baseline accuracy and, therefore, consider our prediction
performance inferior to comparable prior work (Burger et al., 2011; Rao et al., 2010;
Schwartz et al., 2013). With 85.7% accuracy for LIWC features, our models perform in a
similar range of prediction accuracy as comparable LIWC models from prior work with less
imbalanced samples (Jaidka et al., 2018; Schwartz et al., 2013).
Notably, even though all comparable prior research on social media text data was
based on much larger data sets, the performance of our age models is similar to that of past
research (Jaidka et al., 2018; Schwartz et al., 2013). A possible explanation for this
observation could be that self-disclosure in private instant messages is higher compared to
that in public social media posts. Consequently, instant messages could be more informative
of user characteristics, like demographics, than social media posts. Future studies based on
larger data sets should compare predictions from instant messages with those obtained from
social media posts in a similar fashion to Jaidka and colleagues (2018), who compared
models trained on Facebook and Twitter text.
Table 3
Predictive performance for age and gender in comparison to prior work
Data source
Age: MAE/r
Gender: Acc.
(baseline Acc.)
et al.
78.4 (62.0)
91.4 (62.0)
87.5 (62.0)
Jaidka et
al. (2018)
87.0 (56.2)
78.0 (56.2)
91.0 (56.2)
Jaidka et
al. (2018)
81.0 (56.2)
73.0 (56.2)
80.0 (56.2)
Rao et al.
68.7 (50.0)
Burger et
al. (2011)
75.5 (54.9)
85.7 (75.0)
75.0 (75.0)
75.0 (75.0)
Msg. Char.
75.0 (75.0)
75.0 (75.0)
All features
75.0 (75.0)
Note. For comparability, we present studies using the same language features, namely LIWC, n-grams (“words
& phrases”), and/ or topics. Performance measures of the best employed algorithm are reported. All prior
studies are based on English text data while the text data of this work was German. MAE = Mean average error,
Acc. = Prediction accuracy.
4.3. Implications
By investigating age- and gender-linked language variations in instant messages, this
work adds a promising new text source for the study of individual differences in language
usage with relevance for psychology, computer science, linguistics and communication
research. After prior studies had demonstrated that user demographics are predictable from
social media posts, our findings indicate that variation in linguistic characteristics of instant
messages also allows for the accurate prediction of users’ demographics, already in small
samples. Such a demographic user profiling based on instant messages could be used to gain
information on user demographics in order to personalize systems and for marketing efforts,
based on the users’ age and gender. Moreover, it could be useful to validate previously
provided demographic user data. For example, this approach could be used in anonymous
digital communities (e.g., only for people aged under 18) to validate user profiles and to flag
suspicious profiles containing potentially false information (van de Loo et al., 2016). In order
to protect users’ privacy, the feature extraction and potentially model predictions should
happen on the user’s end, for example on the smartphone. Another field of application lies in
determining the demographics of the members of an anonymous community communicating
through instant messaging. By analyzing the characteristics of the messages, one could gain
an approximation of the demographics of such a user population through their unique
psycholinguistic characteristics. This would allow researchers to better understand the
demographics of political or activist movements, and their importance to a respective
These author profiling techniques have the potential for misuse, posing a threat to
user’s privacy and safety in instant messaging. Moreover, in contrast to public posting (e.g.,
on social media platforms), the design of instant messaging services does not suggest that
exchanged information is accessible to third parties. Given the trend that users are
increasingly shifting from social media to instant messaging, the importance of private instant
messaging data is expected to rise further in the future (Goode, 2019). In this manner,
Facebook has announced plans to shift their strategic focus from public posting to private
messaging services (Zuckerberg, 2019) while increasing the efforts to loosen up the privacy
protection of their messaging services (Goodin, 2021). Since commercial collectors of
messaging data have access to much larger quantities of personal communication data, the
monitoring and systematic analysis of private instant messaging environments would allow
for more accurate and additional inferences beyond demographics in a similar way that social
media text has been shown to be informative of, for example, personality traits and emotions
(Preoţiuc-Pietro et al., 2016; Schwartz et al., 2013). The commercial accessibility of these
data will likely enable timely, situation-specific targeting efforts by identifying users’
momentary interests, needs, and desires in private communication. Given our findings that
user characteristics, such as their demographics, can be inferred from instant messages, even
with small training samples, we argue that linguistic data from chat logs should be subject to
extended privacy protection and regulation, similar to older forms of private communication
(e.g., letters) or be clearly labelled as non-private.
4.4. Limitations and outlook
The results of this work are limited in three ways. First, the analyses are subject to the
given data set, which is based on 226 (157 for predictive modelling) German WhatsApp
users’ chat language in 2014/ 2015, and the specific feature extraction methods we applied to
the data. Predictive models from past studies on author profiling from social media text had
been mostly trained on large text samples with thousands of volunteers and millions of words
(Schwartz et al., 2013). Our chat corpus, on the contrary, was much smaller in terms of the
number of volunteers and the available text per volunteer. Further, our data was imbalanced
in a way that there were more female than male volunteers and more volunteers aged 20 to 40
years than outside that age range. As a consequence, our models were less accurate for men
and volunteers aged over 40 years because they had less data to learn from. Also, our models'
predictions got more accurate the more words per volunteer were available (see figure in
repository for detailed results). More data led to more accurate and generalizable models in
past work on author profiling (Eichstaedt et al., in press; Kern et al., 2016; Peersman et al.,
2011). Further, our data set size is likely too small to harness the full potential of data-driven
language features, particularly for topic models. Second, like many studies in the social
sciences, the present work is subject to sampling biases. Specifically, the “What’s up,
Deutschland?” chat corpus consists of chat logs from people, who used WhatsApp, were
aware of the data collection, and also decided to donate their messages for research despite
potential needs for privacy. Therefore, and due to the aforementioned overrepresentation of
young people and women, the data set is not representative for the general public population.
Third, due to a phenomenon termed “concept drift”, which describes how the underlying
association between predictors and the criterion (in our case users’ language usage and their
demographics) changes over time (Lu et al., 2019), our results have to be interpreted in the
context of the time of data collection in 2014/2015. For example, the emergence of emoji
reduced the usage of emoticons in recent years (Pavalanathan & Eisenstein, 2015). This trend
and other developments in instant messaging have changed how men and women of varying
age communicate with time. Therefore, it is necessary to retrain models on newly collected
datasets. While language data from instant messaging is difficult to collect for scientific
research, commercial actors would have access to larger, more representative and
continuously updated samples of private instant messaging data. Hence our findings should
be considered merely a conservative estimate.
We encourage researchers to replicate and extend our study with new data from a
larger and more representative sample to address the limitations, and to further investigate the
predictability of user characteristics from instant messages. In this context it would be
interesting to, for example, investigate whether levels of self-disclosure on public social
media and private instant messaging vary across cultural and national contexts. Therefore, we
would welcome if pre-trained lexica and topic models, like the ones for English social media
posts (Sap et al., 2014; Schwartz et al., 2017), were made available to researchers for more
languages and text sources. Moreover, while this work has exclusively focused on the
message characteristics of the respective users, whose age and gender we aimed to predict,
future research could also investigate the influence of the demographic characteristics of the
chat partners and their message characteristics on the other person’s language. It would be
particularly interesting to collect additional data on the user’s personality, education,
language proficiency, and the relationship to the respective chat partner in order to model
their language more holistically to improve prediction performance, and to evaluate the
potential to infer these characteristics from instant messaging data.
The presented methodological approaches in this work can serve as guidance for
future studies making predictions from small and imbalanced text data sets. Overall, to ensure
comparability of studies, we want to encourage researchers to standardize methodological
procedures in future prediction studies on language data. This includes treating continuous
variables, such as age, in a continuous manner, reporting multiple performance measures
including for the baseline model, and quantifying uncertainty in predictions by describing the
variation in prediction performance across models. Finally, we suggest to investigate trained
machine learning models thoroughly and apply methods to make them interpretable (Molnar,
2019; Stachl et al., 2020).
5. Conclusion
In this work, we identify age- and gender-linked language variations and demonstrate
that user demographics are predictable from WhatsApp instant messages. Our findings
replicate and extend past results on individual differences in social media language to the
growing domain of instant messaging. Further, we provide methodological recommendations
on how to make predictions from small and imbalanced text data sets. We highlight further
research opportunities and emphasize the rising threats to individual privacy that could arise
from the monitoring of formerly private instant messaging environments.
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... However, a prediction model is not necessarily a causal model. There are noteworthy earlier publications predicting age based on personality nuances (Mõttus & Rozgonjuk, 2019), Facebook likes (Kosinski, Stillwell, & Graepel, 2013), and WhatsApp messages (Koch, Romero, & Stachl, 2020). In the present context, age-technically, the time expired since birth-is only a proxy for events that are associated with knowledge acquisition. ...
Although crystallized intelligence (gc) is a prominent factor in contemporary theories of individual differences in intelligence, its structure and optimal measurement are elusive. Analogously to the personality trait hierarchy, we propose the following hierarchy of declarative fact knowledge as a key component of gc: a general fact knowledge factor at the apex, followed by broad knowledge areas (e.g., natural sciences, social sciences, humanities), knowledge domains (e.g., chemistry, law, art), and nuances. In most scientific contexts we are predominantly concerned with aggregate levels, but we argue that the sampling of knowledge items strongly affects distinctions at higher levels of the hierarchy. We illustrate the magnitude of item-level heterogeneity by predicting chronological age differences through knowledge differences at different levels of the hierarchy. Analyses were based on an online sample of 1629 participants between age 18 and 70 who completed 120 broadly sampled declarative knowledge items across twelve domains. The results of linear and elastic net regressions, respectively, demonstrated that the majority of the age variance was located at the item level, and the strength of the prediction decreased with increasing aggregation. Knowledge nuances seem to tap important variance that is not covered by aggregate scores (e.g., sum or factor scores) and that is useful in the prediction of age. In turn, these effects extend our understanding how knowledge is acquired and imparted. On a more general stance, to gain new insights into the nature of knowledge, its optimal measurement and psychometric representation, item and person sampling issues should be considered.
Author profiling from text documents has become a popular task in latest years, in natural language applications. Author profiling is important for various domains such as advertising, marketing, forensics, and security. This survey focuses on profiling age and gender, the two features, which are probably the most researched profile attributes. In this paper, we present an overview of representative studies and datasets of the field (including those organized by PAN) with several significant leaps. Due to the increasing use of deep learning (DL) methods in recent years, we have also reviewed several DL systems that profile authors’ age and gender. Most age and gender datasets contain blog posts or Twitter messages written in English, Spanish or Arabic. There are also several relevant datasets written in Dutch, Italian, Portuguese, Turkish, and Russian. There is no consistency and no uniformity in the datasets concerning to the number and types of their documents, the division into training, dev, and test sets, the types of the applied preprocessing methods, and the quality measures used to evaluate the classification results. A prominent interesting finding is that the best age accuracy results are not as high as we might have expected taking into account relatively simple types of classification especially by gender (only 2 categories) when a large number of teams have competed over the years. Another interesting finding that repeats itself in various classification tasks is that classical ML methods are still better than DL methods for age and gender classification tasks. Most classical systems used word unigrams and bigrams and character 3-4-5-grams. Several systems also used various types of stylistic features. While many earlier systems did not apply preprocessing methods, most recent systems applied several preprocessing methods, e.g., lowercase conversion and replacement of various strings (e.g., URLs, LF characters, and User Mentions). We also suggest several potential future issues in age and gender profiling research.
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Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but these approaches have not been comprehensively compared. To provide guidance on best practices for automatically analyzing written text, this narrative review and quantitative synthesis compares five predominant closed- and open-vocabulary methods: Linguistic Inquiry and Word Count (LIWC), the General Inquirer, DICTION, Latent Dirichlet Allocation, and Differential Language Analysis. We compare the linguistic features associated with gender, age, and personality across the five methods using an existing dataset of Facebook status updates and self-reported survey data from 65,896 users. Results are fairly consistent across methods. The closed-vocabulary approaches efficiently summarize concepts and are helpful for understanding how people think, with LIWC2015 yielding the strongest, most parsimonious results. Open-vocabulary approaches reveal more specific and concrete patterns across a broad range of content domains, better address ambiguous word senses, and are less prone to misinterpretation, suggesting that they are well-suited for capturing the nuances of everyday psychological processes. We detail several errors that can occur in closed-vocabulary analyses, the impact of sample size, number of words per user and number of topics included in open-vocabulary analyses, and implications of different analytical decisions. We conclude with recommendations for researchers, advocating for a complementary approach that combines closed- and open-vocabulary methods. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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The increasing availability of high‐dimensional, fine‐grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.
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Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to r = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used.
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A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.
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Emojis are commonly used as non-verbal cues in texting, yet may also lead to misunderstandings due to their often ambiguous meaning. User personality has been linked to understanding of emojis isolated from context, or via indirect personality assessment through text analysis. This paper presents the first study on the influence of personality (measured with BFI-2) on understanding of emojis, which are presented in concrete mobile messaging contexts: four recipients (parents, friend, colleague, partner) and four situations (information, arrangement, salutory, romantic). In particular, we presented short text chat scenarios in an online survey (N=646) and asked participants to add appropriate emojis. Our results show that personality factors influence the choice of emojis. In another open task participants compared emojis found as semantically similar by related work. Here, participants provided rich and varying emoji interpretations, even in defined contexts. We discuss implications for research and design of mobile texting interfaces.
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With the rise in social media use, emojis have become a popular addition to text-based communication. The sudden increase in the number and variety of emojis used raises questions about how individuals interpret messages containing emojis. To explore perceptions of emoji usage, we conducted a 2 (Sender Gender: Female or Male) × 2 (Emoji Type: Affectionate or Friendly) between-groups experiment to examine the appropriateness and likability of each of four hypothetical text messages sent to a woman from either a male or female coworker. In general, we predicted that text messages containing affectionate emojis (i.e., kissing-face and heart emoji) would be perceived as more appropriate and likable when they came from female than from male senders, whereas messages containing less overtly affectionate (but still friendly) emojis (i.e., smiling-face emoji) would be considered equally appropriate and likable whether it came from female or male senders. As predicted, the results confirmed that texts with affectionate emojis were judged as more appropriate and likable when they came from women than from men. However, texts with less affectionate but friendly emojis were judged as equally appropriate–but more likable–when they came from men than when they came from women. Taken together, our results indicate that gender and emoji choice influence perceptions, and therefore people should consider how emoji choice could impact the reception of their message.
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In this manual, we introduce a new version of the German adaptation of the Linguistic Inquiry and Word Count (LIWC), called the DE-LIWC2015. The aim of the present work was to develop an update to the previous version of the German LIWC adaptation (Wolf et al., 2008) that corresponds to the LIWC2015 properties. The overall goal was to enable automated word count analysis that accurately captures the most frequently used words in the German language context, converting them into psychologically meaningful metrics for use in research.
An online survey, the Understanding Emoji Survey , was conducted to assess how English-speaking social media users interpret the pragmatic functions of emoji in examples adapted from public Facebook comments, based on a modified version of [15]’s taxonomy of functions. Of the responses received (N = 519; 351 females, 120 males, 48 “other”; 354 under 30, 165 over 30, age range 18--70+), tone modification was the preferred interpretation overall, followed by virtual action , although interpretations varied significantly by emoji type. Female and male interpretations were generally similar, while “other” gender respondents differed significantly in dispreferring tone and preferring multiple functions . Respondents over 30 often did not understand the functions or interpreted the emoji literally, while younger users interpreted them in more conventionalized ways. Older males were most likely, and younger females were least likely, to not understand emoji functions and to find emoji confusing or annoying, consistent with previously reported gender and age differences in attitudes toward, and frequency of, emoji use.
Emojis (particularly smiley emojis, ☺) are increasingly used in computer-mediated communication as well as in applied domains within marketing, healthcare, and psychology. The emotional negativity bias in the facial emotion processing literature posits that women are more sensitive to negative facial emotion than are men. Given the similarity in neural processing between human faces and smiley emojis, women may likewise view negative smiley emojis as more negative than do men. Moreover, the familiarity of the emoji and the participants' overall emoji use may increase the positivity of the emoji. To investigate these potential influences of sex, familiarity, and emoji use on the valence of smiley emojis, we assessed the familiarity and the perceived valence for 70 iOS facial emojis in a large sample (N = 299; 163 women) of United States college students (Mage = 19.66, SDage = 2.72). Results indicated higher emoji usage and familiarity ratings for women than for men. In assessing valence we found higher overall positive ratings for men than for women. Consistent with the emotional negativity bias, this sex difference was limited to the negative smiley emojis with no sex difference in valence for the positive emojis. The obtained sex differences in smiley emojis’ use, familiarity, and valence are an important consideration in the selection of such stimuli in future studies.
Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.