# Social Media Unrest Prediction during the COVID-19 Pandemic:
Neural Implicit Motive Pattern Recognition
as Psychometric Signs of Severe Crises

Dirk Johannßen
MIN Faculty
Dept. of Informatics
Universität Hamburg
& Nordakademie
http://lt.informatik.uni-hamburg.de/
{biemann<EMAIL_ADDRESS>

&Chris Biemann
MIN Faculty
Dept. of Informatics
Universität Hamburg
22527 Hamburg, Germany

###### Abstract

The COVID-19 pandemic has caused international social tension and unrest.
Besides the crisis itself, there are growing signs of rising conflict
potential of societies around the world. Indicators of global mood changes are
hard to detect and direct questionnaires suffer from social desirability
biases. However, so-called implicit methods can reveal humans intrinsic
desires from e.g. social media texts. We present psychologically validated
social unrest predictors and replicate scalable and automated predictions,
setting a new state of the art on a recent German shared task dataset. We
employ this model to investigate a change of language towards social unrest
during the COVID-19 pandemic by comparing established psychological predictors
on samples of tweets from spring 2019 with spring 2020. The results show a
significant increase of the conflict-indicating psychometrics. With this work,
we demonstrate the applicability of automated NLP-based approaches to
quantitative psychological research.

##  1 Introduction

00footnotetext:  This work is licensed under a Creative Commons Attribution
4.0 International License. License details:
http://creativecommons.org/licenses/by/4.0/.

The COVID-19 pandemic and the reactions to it have led to growing social
tensions. Guitérrez-Romero [Gutiérrez-Romero, 2020] studied the effects of
social distancing and lockdowns on riots, violence against civilians, and
food-related conflicts in 24 African countries. The author found that the risk
of riots and violence have increased due to lockdowns. Resistance against
national health regulations such as the duty to wear masks are partially met
with resistance by movements such as anti-maskers or anti-obligation
demonstrations.111https://firstdraftnews.org/latest/coronavirus-how-pro-mask-
posts-boost-the-anti-mask-movement/ Even anti-democratic alterations of e.g.
services offered by the US Postal Service (USPS) of delivering mail-in ballots
for the US presidential elections 2020, which are essential for social
distancing measures amidst the pandemic, are being utilized amidst this
international crisis and foster social unrest and potential outbursts of
violence, civil disobedience or
uprisings.222https://www.businessinsider.com/trump-walks-back-threat-block-
covid-relief-over-usps-funding-2020-8?r=DE&IR=T

Social media has become an important reflection of nationally and
internationally discussed topics, and is a predictor of e.g. stock markets,
disease outbreaks or political elections [Kalampokis et al., 2013]. The
majority of human-produced data exists in textual form and broadly in social
media and thus, an the investigation of social unrest and conflict situations
from social media becomes a worthwhile application area for natural language
processing (NLP) problems [Gentzkow et al., 2019].

When speaking about such global phenomena such as a rise in international
social unrest and possible occurrences of conflict reflected in text, the
detection of specific keywords or utterances have not been successful in past
research. Mueller et al. [Mueller and Rauh, 2017] utilized Laten Dirichlet
Allocation (LDA, [Blei et al., 2003]) topic modelling on war-related newspaper
items and were not able to improve predictability from other multi-factor
models that take into account e.g. GDP figures, mountainous terrain or ethnic
polarization. Furthermore, Chadefaux [Chadefaux, 2012] showed that news
reports on possible war situations alone did not function as good predictors
but identified sharp frequency increases before war emerged, possibly helping
with just-in-time safety measures but likely failing to avoid war situations
altogether.

Alternatively, the risks of escalation could be determined based on
politician’s personalities and the current mood and tone of utterances
[Schultheiss and Brunstein, 2010, p. 407]. However, intrinsic desires and
personality can hardly be measured directly (see Section 3). Intrinsic or
subconscious desires and motivation would more likely correlate with
personalities, tone, and thus possibly social unrest.

We hypothesize that the frequency of social unrest predictors have
significantly changed in social media textual data during the COVID-19
pandemic drawn from the Twitter 1 percent
stream333https://developer.twitter.com/en/docs/labs/sampled-stream/overview in
early 2019 and 2020, whilst linguistic features stay comparably stable and
unchanged. With this, we aim to demonstrate a possible transition from
laborsome manual psychological research to automated NLP approaches.

After presenting and discussing related work in Section 2, we will first
introduce the concept of implicit motives and self-regulating levels in more
details in Section 3 and the social unrest predictors thereafter in Section 4.
The data utilized for experiments is described in Section 5 and the
methodology in Section 6. Thereafter, we will present the results in Section 7
and discuss their impacts in Section 8. Lastly, we will draw a conclusion in
Section 9.

##  2 Related Work

Conflict predictions from natural language are rarely encountered applications
and have mainly been about content analysis and less about crowd psychology.
Kutuzov et al. [Kutuzov et al., 2019] used one-to-X analogy reasoning based on
word embeddings for predicting previous armed conflict situations from printed
news. Johansson et al. [Johansson et al., 2011] performed named entity
recognition (NER) and extracted events via Hidden Markov Models (HMM) and
neural networks, which were combined with human intelligence reports to
identify current global areas of conflicts, that, in turn, were utilized
mainly for world map visualizations.

Investigation of personality traits has mainly been focussing on so-called
explicit methods. For these, questionnaires are filled out either by
interviewers, through observations, or directly by participants. One of the
most broadly utilized psychometrics is the Big Five inventory, even though its
validity is controversial [Block, 1995]. The five-factory theory of
personality (later named Big Five) identifies five personality traits, namely
_openness to experiences_ , _conscientiousness_ , _extraversion_ ,
_agreeableness_ and _neuroticism_ [McCrae and Costa Jr., 1999, Goldberg,
1981]. This Big Five inventory was utilized by Tighe and Chegn [Tighe and
Cheng, 2018] for analyzing these five traits of Filipino speakers.

Some studies perform natural language processing (NLP) for investigating
personality traits. Lynn et al. [Lynn et al., 2020] utilized an attention
mechanism for deciding upon important parts of an instance when assigning the
five-factor inventory classes. The Myers-Briggs Type Indicator (MBTI) is a
broadly utilized adaption of the Big Five inventory, which Yamada et al.
[Yamada et al., 2019] employed for asserting the personality traits within
tweets.444A _Tweet_ is a short message from the social network microblogging
service Twitter (https://www.twitter.com/) and consists of up to 240
characters.

The research field of psychology has moved further towards automated language
assertions during the past years. One standard methodology is the utilization
of the tool linguistic inquiry and word count (LIWC), developed by Pennebaker
et al. [Pennebaker et al., 1999]. The German version of LIWC was developed by
Wolf et al. [Wolf et al., 2008]. It includes 96 target classes, some of which
are rather simple linguistic features (word count, words longer than six
characters, frequency of punctuation), and psychological categories such as
anxiety, familiarity, or occupation. Even though the tool appears rather
simple from an NLP point of view, it has a long tradition to be utilized for
content research in the field of behavioral psychology. Studies utilizing LIWC
have shown that function words are valid predictors for long-term developments
such as academic success [Pennebaker et al., 2014]. Furthermore, it has been
shown that LIWC correlates with the Big Five inventory [McCrae and Costa Jr.,
1999]. Importantly, the writing style of people can be considered a trait, as
it has shown high stability over time, which means that it is not dependent on
one’s current mood, the time of day, or other external conditions [Pennebaker
and King, 2000]. Hogenraad [Hogenraad, 2003] utilized an implicit motive (see
Section 3) dictionary approach to automatically determine risks of war
outbreaks from different novels and historic documents, identifying widening
gaps between the so-called power motive and affiliation motive in near-war
situations.

Overall, the work on automated classification of implicit motive data or the
use of NLP for the assertion of psychological traits in general is rather
sparse or relies on rather outdated methods, as this application domain can be
considered a niche [Schultheiss and Brunstein, 2010, Johannßen and Biemann,
2019, Johannßen and Biemann, 2018, Johannßen et al., 2019]. One recent event
in this area was the GermEval 2020 Task 1 on the Classification and Regression
of Cognitive and Motivational Style from Text [Johannßen et al., 2020]. The
best participating team reached a macro f1 score of 70.40 on the task of
classifying implicit motives combined with self-regulating levels, resulting
in 30 target classes. However, behavioral outcomes from automatically
classified implicit motives have – to our knowledge – not yet been researched.

##  3 Implicit Motives and Self-regulatory Levels

Implicit motives can reveal intrinsic, unconscious human desires, and thus
avoid social desirability biases, which usually are present when utilizing
direct questionnaires. They originated from the Thematic Apperception Test
(TAT) by Murry et al [Murray, 1943]. Participants are confronted with
ambiguous images of multiple people that interact with each other as displayed
in Figure 1, and are asked to answer four questions: i) who is the main
person, ii) what does that person feel? iii) why does the person feel that
way, and iv) how does the story end? From these questions, trained
psychologists can assign one of five motives: affiliation (A), freedom (F),
achievement (L), power (M), and zero (0). The psychologists follow some rules,
one being the so-called primacy rule, where the very first identifiable motive
determines the whole instance, despite what follows [Scheffer and Kuhl, 2013].
These motives have shown to be behavioral predictors and allow for long-term
statements of e.g. group dynamics or success [McClelland and Boyatzis, 1982,
Schultheiss and Brunstein, 2010]. Implicit motives have been broadly utilized
in the 1980s but at the cost of laborsome manual annotating processes. It
takes about 20 hours of training for an annotator to encode one of the
implicit motives. Skilled human annotators take up to 50 hours per 100
participants. This costly annotating process has hampered this once-promising
psychometric [Schultheiss and Brunstein, 2010, p. 140].

Whilst the classification performance of implicit motive models have been
explored and achieved high results (e.g. [Johannßen and Biemann, 2019,
Johannßen et al., 2020]), behavioral consequences and mass phenomena from
automated labeled textual instances have barely been researched.

In addition to the implicit motives, the data set from the GermEval20 Task 1
comes with so-called levels per textual instance. The levels were developed by
Kuhl [Kuhl, 2001]. They describe the self-regulatory enactment in five
dimensions. According to Scheffer and Kuhl [Scheffer and Kuhl, 2013] the 1st
level is the ability to self-regulate a positive affect, the 2nd is the
sensitivity for positive incentives, the 3rd self-regulates a negative affect,
the 4th is the sensitivity for negative incentives and the 5th level describes
the passive coping with fears. In other words, these levels help to identify
the type of the participant’s emotional response according to the identified
implicit motive.

As with many psychometrics, the reliability of implicit motives, and
especially their predecessor (the TAT) is controversial. One main point of
criticism is that implicit motives do not correlate significantly with so-
called explicit motives. Whilst implicit motives try to measure intrinsic
desires indirectly by asking participants associative questions, explicit
motives try to measure desires via direct questionnaires. In psychology,
reliability means, that personality traits revealed by one measure may not
conflict with personality traits measured by other, well-established measures.
Since the measured desires of implicit and explicit motives generally do not
match, the reliability of implicit motives is said to be weak.

Schultheiss et al. [Schultheiss and Brunstein, 2010] explain this lack of
reliability and correlation with the fact that explicit implicit motives are
by definition of different measurements that can not be directly compared.
Whilst implicit motives measure intrinsic desires that are subconscious,
explicit motives are more influenced by a social expectation bias (i.e. what
do participants think is a socially sound and accepted answer to a question)
and thus are closer connected to behaviorism. Nonetheless, reliability in
psychological research demands different observable results of metrics to be
coherent [Reuman, 1982] when the descriptions of what a psychometric is
supposed to measure matches (i.e. desires).

Another point of criticism is the way the TAT images are selected. They emerge
from an empirical study, where participants are shown different images. Only
when past frequencies of motives are achieved with an image, this image gets
added to the available testing stock. With this, however, the very first
selected implicit motive images could not have been validated. Nowadays, many
scholars argue that the amounts of positive evidence legitimize this
methodology, but it has yet to be resolved [Hibbard, 2003].

Figure 1: Exemplary image to be interpreted by participants utilized for the
operant motive test (OMT). Identifiable motives are the affiliation motive
(A), the power motive (M), achievement (L), and freedom (F). A 0 represents
the unassigned motive [Kuhl and Scheffer, 1999].

The transition from natural language to intrinsic desires and motivation is
not trivial, as humans do not express intrinsic and unconscious desires
unfiltered and directly. As soon as a direct questionnaire is involved, social
desirability biases (i.e. thoughts of publicly expected answers) alter an
uninfluenced introspection [Brunstein, 2008]. Such direct questionnaires are
called explicit methods, in contrast to implicit methods, such as e.g. the TAT
and subsequent tests produced by image descriptions.

##  4 Social Unrest Predictors

Times of severe social unrest are reflected by distinct patterns of implicit
motives and linguistic features. Winter [Winter, 2007] surveyed multiple prior
studies, identifying three main predictors: _responsibility_ , _activity
inhibition_ , and _integrative complexity_ , displayed in Table 1. In this
study, the author identified and analyzed 8 occurrences of crises and social
unrest by examining influential political speeches of this time. Thereafter,
the outcomes of these crises – whether they ended peacefully or in conflict –
were projected on indicators from earlier research.

Winter and Barenbaum [Winter and Barenbaum, 1985] found that the power motive
(M) has a moderating effect of responsibility. In other words, responsibility
determines, how vast amounts of power motivated expressions are behaviorally
enacted. If a high responsibility score is measurable, power motivated
individuals act pro-social. On the contrary, if the responsibility score is
low, aggression and lack of leadership are to be expected.

Category |  Measure |  Example or Explaination
---|---|---
Responsibility
i) moral standards |  observable, if people, actions, or things are described with either morality or legality |  ’she wants to do the right thing’
ii) obligation |  means, that a character in a story is obliged to act because of a rule or regulation |  ’he broke a rule’
iii) concern for others |  emerges, when a character helps or intends to help others or when sympathy is shown or thought |  ’the boss will understand the problem and will give the worker a raise’
iv) concerns about consequences |  can be identified when a character is anxious or reflects upon negative outcomes |  ’the captain is hesitant to let the man on board, because of his instructions’
v) self-judgment |  scores when a character critically judges his or her value, morals, wisdom, self-control, etc. and has to be intrinsic |  ’the young man realizes he has done wrong’
Activity inhibition
linguistic negation |  in English terms, the authors describe activity inhibition as the frequency of “not” and “-n’t” |  responsibility measure, e.g. a variable negatively correlated with male alcohol consumption
leadership motive pattern (LMP) |  combined with a high power motive (M) and low affiliation motive (A) |  predicts responsible leadership power behaviors instead of profligate impulsive expressions of power
Integrative complexity
7-point continuum range score from simplicity to differentiation and integration |  1: no sign of conceptual and differentiation or integration can be observed |  only one solution is considered to be legitimate
7: overreaching |  viewpoints are expressed, involving different relationships between alternate perspectives
Table 1: According to Winter [Winter, 2007], some distinct psychometrics and
their combinations predict social unrest – namely responsibility, activity
inhibition, and integrative complexity. The table shows their categories,
measurements and offer examples or explanations. Responsibility is measured
with a dedicated TAT, activity inhibition (AI) and integrative complexity is
determined via content analysis. Especially the combination of low
responsibility, high activity inhibition and little integrative complexity
(e.g. high frequency of the power motive combined with the self-regulatory 4th
level) predict situations of social unrest with negative escalatory outcomes.

_Activity inhibition_ is reflected, according to by McClelland et al.
[McClelland and Davis, 1972] as the frequency of “not” and “-n’t”
contradictions in TAT or other verbal texts. Activity inhibition functions as
motivational and emotional regulation. The authors identified a negative
correlation between activity inhibition and male alcohol consumption. Combined
with a high power motive (M) and low affiliation motive (A), subsequent
research by McClelland and his colleagues revealed a so-called leadership
motive pattern (LMP) [McClelland and Boyatzis, 1982, McClelland, 1988]. The
higher this LMP, the more responsible leaders act. As for _integrative
complexity_ it was observed, that the lower the frequency of utterances in
accordance to the 7-point score was (see Table 1), the more likely escalations
became.

##  5 Training and experimental data

For testing the proposed hypothesis (Section 1), we first train a
classification model and utilize this model for testing social network textual
data. In this section, we will describe the two different data sources for
training and the experiments.

###  5.1 Model Training Data

The data utilized for training models were made available by the organizers of
the _GermEval-2020 Task 1 on the Classification and Regression of Cognitive
and Motivational Style from Text_ 555GermEval is a series of shared task
evaluation campaigns that focus on Natural Language Processing for the German
language.666%****␣coling2020.tex␣Line␣475␣****https://www.inf.uni-
hamburg.de/en/inst/ab/lt/resources/data/germeval-2020-cognitive-motive.html
[Johannßen et al., 2020]. The training set consists of 167,200 unique answers,
given by 14,600 participants of the OMT (see Section 3). The training data set
is imbalanced. The power motive (M) is the most frequent class, covering
41.02% of data points. The second most frequent class, achievement (L) only
accounts for 19.63% and thus is half as frequent as M. The training data was
assembled and annotated by the University of Trier, reaching a pairwise
annotator intraclass correlation of r = .85. With only 22 words on average per
training instance (i.e. a participant’s answer) and a standard deviation of 12
words, training a classifier on this data is a short text classification task
[Johannßen et al., 2020].777The data can be retrieved via https://www.inf.uni-
hamburg.de/en/inst/ab/lt/resources/data/germeval-2020-cognitive-motive.html

###  5.2 Experimental Data

The experimental data was collected before this work by crawling the Twitter
API and fetching 1 percent of the worldwide traffic of this social network
[Gerlitz and Rieder, 2013]. We sample posts over the time window from March to
May of both, 2019 and 2020. There are no apparent linguistic differences
between the two samples. The average word count, part-of-speech (POS) tags,
sentence length, etc. are comparable.

Thereafter, we extracted the _text_ and _date time_ fields of posts marked as
German. From those files hashtags, name references (starting with ’@’),
corrupted lines, and any post shorter than three content words were removed.
The resulting files for 2019 and 2020 contained more than 1 million instances.
Lastly, the instances were randomly shuffled. We drew and persisted 5,000
instances per year for the experiments, as this data set size is large enough
for producing statistically significant results. The posts on average consist
of 11.97 (2019) and 11.8 (2020) words per sentence, and thus are very short.
During the experiments, further pre-processing steps were undertaken, which
are described in Section 6. By stretching out the data collection time window
and by comparing the same periods in two subsequent years, we aim to reduce
any bias effect that might impact Twitter user behavior over short periods,
e.g. the weather, any sports event, or short-lived political affairs.

##  6 Methodology for Implicit Motive Classification Social Unrest Prediction

For constructing a model of sufficient quality to test our hypothesis, we
follow Johannßen and Biemann [Johannßen and Biemann, 2019] and train a long
short-term memory network (LSTM, [Hochreiter and Schmidhuber, 1997]) combined
with an attention mechanism.

An LSTM is a special type of recurrent neural network (RNN). An RNN not only
has connections between units from layer to layer but also between units of
the same layer. Furthermore an LSTM also has a mechanism called the _forget
gate_ , allowing the structure to determine which information to keep and
which information to forget during the training process. The attention
mechanism [Young et al., 2018] can capture the intermediate importance of
algorithmic decisions made by the network. It can be employed for enhanced
results but also investigated for researching algorithmic decisions. However,
it is debated upon, whether this algorithmic importance can serve as an
explanation. Even though oftentimes, the algorithmic importance is correlated
with an explanation for the task (i.e. does a model for image recognition of
animals _look_ at the animals or the backgrounds of the images?), there are
cases, where algorithmic importance and explanation for the task differ [Jain
and Wallace, 2019, Wiegreffe and Pinter, 2019]). Since automatically labeling
implicit motives is a sequential problem revolving around identifying the
first verbal enactment of a motive (see Section 3, we decided to employ a Bi-
LSTM with an attention mechanism [Schuster and Paliwal, 1997].

We decided against additional features such as part of speech (POS) tags or
LIWC features like in our previous work [Johannßen and Biemann, 2019], as we
did not reach the best results with these additional features. The maximum
token length was set to 20, as determined by preliminary experiments
[Johannßen and Biemann, 2019], and reflects the primacy rule of the implicit
motive theory described in Section 3. The average answer length of the
training data set was 22 tokens (see Section 5). With this decision to limit
the considered tokens, we aim to closely replicate the implicit motive coding
practices manually performed by trained psychologists [Kuhl and Scheffer,
1999]. Accordingly, it is preferable to assign the 0 motive (i.e. no clear
motive could be identified) than to falsely assign a motive that is not the
very first one in the sequence.

Some standard pre-processing steps were applied to reduce noise, which was to
remove the Natural Language Toolkit (NLTK) German corpus stop words888The
Natural Language Toolkit (NLTK) is a collection of python libraries for NLP
https://www.nltk.org/., to lowercase the text, remove numbers, normalize
special German letters (i.e. umlaute). Emojis were removed as well, since
Twitter offers a selection of 3,348
emojis999https://emojipedia.org/twitter/twemoji-13.0.1/ , that in turn mainly
do not capture sufficient informational gain per textual answer for the task
at hand. To remove stop words has to be an informed choice when it comes to
performing NLP on psychological textual data. For example, function words are
said to predict academic success (see Section 2). However, during our
experiments, we saw an increase in model performance when stop words were
removed.

After the training, we utilize the model on the two sampled data sets
described in Subsection 5.2. According to our hypothesis in Section 1 and
following the theories in Section 4, we investigate the frequency of the power
motive with the self-regulatory level 4, which we expect to be higher. At the
same time, we will also analyze the other motives and levels to see which ones
are now less frequent and to what extent. Furthermore, we compare different
linguistic features and statistics from 2019 to 2020 to see, if any of these
show differences that might indicate possible biases in the data.

Our Bi-LSTM model was set to be trained within 3 epochs and with a batch size
of 32 instances. The model was constructed having 3 hidden layers and utilized
pre-trained fasttext embeddings [Bojanowski et al., 2017], as this character-
based or word fragment-based language representation has shown to be less
prone to noisy data and words that have not been observed yet like e.g.
spelling mistakes or slang – both often observable in social media data. The
fasttext embeddings had 300 dimensions and were trained on a Twitter corpus,
ideally matching the task at hand.101010The fasttext model was obtained from
Spinningbytes at http://spinningbytes.ch/resources/wordembeddings Explorative
experiments with different parameter combinations have shown that a drop-out
rate of .3 and step width of .001 produced good results.

The cross-entropy loss was reduced rather quickly and oscillated at 1.1 when
we stopped training early during the second epoch. After each epoch, the model
was evaluated on a separate development test. After the training was finished,
the model was tested once on the GermEval20 Task 1 test data and with the
official evaluation script. This provides the chance to compare the achieved
results with the best-participating team. Schütze et al. [Schäfer et al.,
2020] achieved a macro f1 score of 70.40, which our Bi-LSTM model was able to
outperform with an f1 score of 74.08, setting a new state of the art on this
dataset.

##  7 Results

After having trained the Bi-LSTM model and sampled the experimental data, we
will describe the results and findings of the conducted Twitter COVID-19
experiments in this section. An overview of all results is displayed in Table
2.

Metric | 2019 | 2020 | Percentage delta | Significance
---|---|---|---|---
Activity inhibition and responsibility
Power 4 | 33.76 | 37.40 | 10.97 | p<.01***
LIWC Family | .08 | .05 | -37.60 | p<.05*
LIWC insight | .23 | .17 | -26.09 | p<.05*
Implicit motives
Power motive | 65.84 | 68.24 | 3.64 | p<.01***
Freedom motive | 20.28 | 17.72 | -12.63 | p<.01***
Achievement motive | 6.80 | 7.00 | 2.94 | p>.05
Affiliation motive | 2.00 | 1.86 | -7.00 | p>.05
Null motive | 5.10 | 5.10 | .00 | p>.05
Self-regulatory levels
Level 1 | 6.50 | 6.01 | -7.54 | p>.05
Level 2 | 2.76 | 3.26 | 18.12 | p>.05
Level 3 | 27.20 | 25.58 | -5.96 | p<.01***
Level 4 | 42.78 | 45.20 | 5.67 | p<.01***
Level 5 | 15.86 | 14.92 | -5.93 | p<.05*
Linguistic statistics
Average words | 11.97 | 11.80 | -1.42 | p>.05
Verbs | 1.19 | 1.22 | 2.52 | p>.05
Adjectives | .43 | .43 | .00 | p>.05
Words >6 letters | 38.65 | 38.86 | .54 | p>.05
Table 2: Overview of the different psychometric and statistical results. *
represents significant results, *** represents highly significant results. All
combinations of motives and levels have been examined. Note that most motives,
levels, and statistical values stay constant. However, power 4 is more
frequent, whilst the freedom motive is less. As the linguistic statistic
metrics stay relatively stable, this indicates no observable sampling bias.

To investigate the main predictor for social unrest _activity inhibition_ (see
Section 4), the power motive (M) in combination with level 4 was counted. The
self-regulatory level 4 describes the sensitivity for negative incentives (see
Section 3). These measures are collected for all four data sets. Our Bi-LSTM
model assigned power 4 in 33.76% of all cases for the Twitter sample from
March to May of 2019, making this the most frequent label. However, for the
data sample from 2020, power 4 is as frequent as 37.4%, making this an
increase of 10.97%. For calculating the significance of this rise, we perform
a t-test on the label confidences for the power motive with self-regulatory
level 4 for both, 2019 and 2020 with the 5,000 samples from each year (see
Section 5).

The two-sample t-test on the confidence levels shows, that the rise in
frequency is statistically significant ($p<0.05$ with $\bar{x}_{1}=.27$,
$\bar{x}_{2}=.29$, $\sigma_{1}=.28$, $\sigma_{2}=.28$, $N_{1}=5,000$ and
$N_{2}=5,000$).

The affiliation motive (A) is barely classified, covering only 2% (2019) and
1.89% (2020) of all instances. The slight decrease is not statistically
significant ($p>.05$). The frequency of self-regulatory level 4 is elevated by
6.7%. The whole of all assigned power motive labels has only risen by 3.64%,
both having risen less than the combination of the power motive and level 4
combined. The strongest decline in frequency can be measured for the freedom
motive with -12.63%. The other motives of affiliation, achievement, and null
have barely changed in comparison to 2019 with 2020. The same holds for the
average amounts of words per sentence, verbs, adjectives, and words containing
at least 6 letters, all of which have barely changed, not indicating sampling
biases. An overview of the class frequencies is provided in Table 3.

Implicit motive | Frequency | Self-regulatory level | Frequency
---|---|---|---
2019
Power | 3,251 | 1 | 492
Affiliation | 141 | 2 | 193
Achievement | 414 | 3 | 1,487
Freedom | 9622 | 4 | 1,872
Zero | 232 | 5 | 724
|  | 0 | 232
2020
Power | 3,433 | 1 | 316
Affiliation | 90 | 2 | 151
Achievement | 203 | 3 | 1,259
Freedom | 923 | 4 | 2,233
Zero | 761 | 5 | 780
|  | 0 | 261
Table 3: Overview of the class frequencies.

Since both, responsibility and integrative complexity can only be measured by
employing a specific TAT and a questionnaire, which would have to be performed
with each Twitter user, we can only investigate activity inhibition as a
combination of the power motive with the self-regulatory level 4. However, we
will review some psychological LIWC categories, that follow a close
description as the five categories of Winter’s responsibility scoring system
[Winter and Barenbaum, 1985]. Relevant LIWC categories for the responsibility
is the combination of _family_ , which are terms connected to expressions like
’son’ or ’brother’, and _insight_ , which contain expressions such as ’think’
or ’know’, representing self-aware introspection. Family shows a significant
decrease from 2019 (0.08) to 2020 (0.05) of -37.5%. The frequency of insight
terms fell from 2019 (.23) to 2020 (.17) by -26%, all of which are
statistically significant changes ($p<0.05$ for both categories).

##  8 Discussion

We hypothesized that the social unrest predictors by Winter [Winter, 2007],
namely _activity inhibition_ , _responsibility_ , and _integrative complexity_
are automatable and reveal changes in natural language and signs of social
unrest observable through the use of social media textual data connected to
the COVID-19 pandemic.

The main research objective of this work is to find novel approaches to
automatically provide the community with red flags for growing tensions and
signs of social unrest via social media textual data. For this, _activity
inhibition_ is the main predictor. It consists of a distinct shift in implicit
motives. It is present when the frequency of the power motive with the self-
regulating level 4 (sensitivity for negative incentives, see Section 3) is
elevated and the affiliation motive is suppressed – even though Winter
[Winter, 2007] did not find clear evidence of the latter. The comparable rise
by 10.97% ($p<0.01$) is an indicator of the social tension of COVID-19 related
social media posts.

Since other linguistic statistics, such as the average amounts of adjectives,
verbs, words per sentence, or words containing at least 6 letters have barely
changed, this indicates that the measurable differences in social unrest
predictors are content-based and not due to linguistic biases.

It is remarkable, that whilst the power motive has been labeled more
frequently, the frequency of the labeled freedom motive has declined by
-12.63% from 2019 to 2020. This freedom motive has barely been researched yet
but has a close connection to the power motive. Whilst power-motivated
individuals desire control over their fellow humans and their direct
surrounding for the sake of control, freedom-motivated individuals seek to
express themselves and want to avoid any restraining factors. Motives are said
to be rather stable but can change over time [Schultheiss and Brunstein,
2010]. This change in motive direction could indicate a roughening of verbal
textual content and interpersonal communication. Example utterances classified
as freedom and power from 2019 compared with 2020 are displayed in Table 4.

The change of responsibility, as reflected in LIWC categories, retreated by
roughly 30% from 2019 to 2020. This responsibility indicates a personal
involvement in topics and decisions, that we feel are relevant for our
surroundings. If this involvement diminishes, our interest in participating in
constructive solutions to problems does as well.

⬇ M Corona-Regeln im Saarland sind zum Teil absurd und unverhältnismäßig F RT
@kattascha: In den USA bekommen viele Menschen keine Lohnfortzahlung im
Krankheitsfall. Das bedeutet: Selbst bei Verdacht auf #COVID19 w... A Wer
einen Discord-Server sucht, um entspannt mit seinen Kollegen zu zocken oder
gemeinsam abzuhängen ist hier genau... \------- Translation \----- M the
Corona rules for the Saarland are partially absurd and dis- proportionate F RT
@kattascha: in the US a lot of people don’t receive continued pay in case of
illness. That means: even in case of suspected #COVID19 A Whoever is looking
for a Discord server for enjoyably game with their colleagues or chill
together, is in the right place...
---
Table 4: Some example tweets from 2019 (left) compared with 2020 (right).
Whilst the power motive (M) is more frequent in 2020, the freedom motive (F)
became less frequent. The affiliation motive (A) was very infrequent in both,
2019 and 2020\. This signature indicates increased social unrest in 2020 in
comparison with 2019.

##  9 Conclusion and Outlook

With this work, we conducted a first attempt at automating psychometrics for
investigating social unrest in social media textual data. The Bi-LSTM model
combined with an attention mechanism of this work achieved an f1 score of
74.08 on 30 target classes, making it state of the art on a respective recent
shared task dataset. With this model, we measured a statistically significant
rise in the power motive with self-regulating level 4, which reflects the
social unrest predictor of _activity inhibition_ in the direct comparison of
the samples from March to May of 2019 vs. 2020.

Furthermore, we investigated _responsibility_ , which shows significant
reductions during the COVID-19 pandemic, hinting at negative outcomes of
interpersonal and verbal communication on the social media platform Twitter.

This first approach most likely does not qualify for a real-world social
prediction system. Predictions of such a system can not yet be reliable enough
for deriving necessary actions from them. On the upside, implicit motives do
not only qualify for examining general socio-economic tensions, but can be
applied on an individual or small group scale. As an example, detecting
tensions within a small group can help to shape the group and guiding it into
a better fit. Furthermore, we advocate for combining implicit motives with
sufficiently many complementary psychometrics and content-based analysis e.g.
sentiment analysis, topic modeling, or emotion detection.

Besides those combinations with other information sources for future work,
different sampling approaches and larger data set sizes should be utilized for
reproducing findings and research correlations with other social unrest
predictors and indicators. In this work, we have made the first steps towards
understanding the automation of psychological findings. Since only 5,000
samples were drawn from a single social network platform, we advocate for
broadening this approach to include many more samples from wider time windows
paired with mixing the data sources. In addition to that, deeper
investigations into the linguistic variances between times of so-called social
unrest and more peaceful times should be performed, as those could reveal
patterns and characteristics of time-specific utterances.

Even though this work is only introductory, the observed correlations and
social unrest patterns are in line with an intuitive assumption of how
language in social media data changes amid a pandemic. Future work arises in
the application of this methodology on other events and crises, eventually
providing a quantitative basis for implicit motive research.

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