I miss you babe: Analyzing Emotion Dynamics During COVID -19 Pandemic (2020)

Abstract: With the world on a lockdown due to the COVID-19 pandemic, this paper studies emotions expressed on Twitter. Using a combined strategy of time series analysis of emotions augmented by tweet topics, this study provides an insight into emotion transitions during the pandemic. After tweets are annotated with dominant emotions and topics, a time-series emotion analysis is used to identify disgust and anger as the most commonly identified emotions. Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states. Observing user patterns through clustering of user longitudinal analyses reveals emotional transitions fall into four main clusters: (1) erratic motion over short period of time, (2) disgust -> anger, (3) optimism -> joy. (4) erratic motion over a prolonged period. Finally, we propose a method for predicting users subsequent topic, and by consequence their emotions, through constructing an Emotion Topic Hidden Markov Model, augmenting emotion transition states with topic information. Results suggests that the predictions fare better than baselines, spurring directions of predicting emotional states based on Twitter posts.

## introduction in august 2020, the covid-19 pandemic raged throughout the world. caused by the virus sars-cov-2 @xcite , the pandemic, still ongoing during this study, has infected more than 26 million people and has taken over 800,000 lives. globally, countries have taken unprecedented measures for virus containment, most notably, implementing cities lockdown and forcing citizens into isolation. despite limited face interactions, digitalization has provided a space for expression. twitter users have expressed their yearning for loved ones, their lifestyles, the freedom of social activities and travels: "[...] i miss you babe. i wish this shit is over and we will travel himalay together!. others express frustration about missed opportunities and the stress of isolation: #covid [..] will kill but #starvation will definitely kill. previous studies of emotion analysis on twitter largely focused on identifying emotions for general tweets @xcite @xcite , and during the covid-19 period @xcite . in analyzing emotion dynamics on twitter, @xcite modelled emotional states of twitter users with a hidden markov model, and further showed that twitter users change their emotional state against their topics @xcite . our study aims to characterize emotional states and transitions of twitter users during the global covid-19 lockdown. the study contributes to ongoing literature through the following research questions: (1) what are the emotion dynamics expressed amid the covid-19 pandemic? that is, what are the topics discussed and the corresponding emotions? (2) what are the profiles of emotion transitions over time? (3) how might we effectively predict a user's subsequent tweet's emotion? to investigate the above questions, we collected a twitter dataset during the covid-19 pandemic (section 3) to study emotion dynamics, which is the interplay between topics discussed and emotions expressed in a tweet. we computationally annotated our collected tweets with emotions from an emotion classifier pre-trained on an labeled emotion dataset @xcite . to augment the emotion label, we learned topics of tweets using latent dirichlet allocation @xcite . in section 4, we study user emotion transitions by first constructing an aggregate emotion transition graph across all users, representing emotions as nodes and edges as emotion transitions between user tweets. we then cluster users' emotion transition graphs and observe emotional transition patterns. in section 5, we propose an emotion topic hidden markov model (et-hmm), adopting a hidden markov model @xcite on the users' emotion transition states augmented with topic information, and evaluate the model's prediction of the topic and emotion expressed in a user's subsequent tweet. our findings, as discussed in section 6, suggest that the et-hmm outperform baselines, and are hopefully useful in monitoring emotional health through social media. ## related work emotion analysis in text is a widely studied area, spanning the evolution of emotions, emotion models, emotion detection methods and construction of datasets, detailed in several surveys highlighting the methods and challenges @xcite @xcite . twitter, a text-rich social media, is a valuable and popular data source for emotion analysis (sailunaz and alhajj, 2019; @xcite . previous studies have proposed methods to detect and analyze twitter users' emotional responses towards an event @xcite and understand users' mental health @xcite . colneriĉ and demsar (2018) investigated and compared classification methods to recognize eight expressed emotions: joy, sadness, trust, disgust, fear, anger, surprise, anticipation. besides classifying user emotions on twitter, existing studies analyzed emotion dynamics and changes of user emotions over time. naskar et al. (2020) investigated social dynamics of emotions in twitter users' opinions, and attempted to change user emotions towards social issues over time. during the pandemic, mukherjee et al. (2020) analysed emotion trends on indian tweets and the influence of public emotions on individual emotions, while gupta et al. (2020) performed emotion and topical analysis on over 63 million tweets. naskar et al. (2019) proposed a hidden markov model (hmm) to model emotional changes through a twitter user's consecutive sequence of tweets. this study contributes to ongoing emotion analysis research by characterizing twitter users' emo-tion dynamics during the covid-19 pandemic. more specifically, we provide a preliminary understanding of the transition of emotional and mental states as the global pandemic develops. to the best of our knowledge, this is the first study that examines emotional dynamics amid a pandemic event. ## data collection we adopt the twitter dataset collected in @xcite . specifically, we begin with a random sample of 5.4 million tweets from 10.1 million users taken from the 1% twitter streaming api collected during a period of seven weeks beginning from march 22, 2020. the tweets were collected with the following hashtags: '#covid-19', '#covid-19', '#covid', '#corona', '#coronavirus', '#coronavirus', '#coronavirus'. from these tweets, we selected for tweets that contained the english words related to "yearning" and their related word forms (i.e. miss, yearn, pine, long, wish, crave), so as to ensure that a substantial number of tweets express some emotions (i.e. do not classify as neutral), and a spectrum of emotions is expressed. in this first selection of data, we obtained 2.8 million tweets from 233,932 users. using the selected set of users, we extracted all their tweets during our collection period, to facilitate longitudinal user emotion dynamic studies. this process yielded us a total of 3,035,844 tweets from 233,932 unique users. we thus use this consolidated dataset for further analysis and experiments. ## characterizing emotion dynamics 4.1 topic discovery to discover topics in the datasets, we apply the mallet lda topic modelling tool @xcite to learn topic clusters and their corresponding keywords. each topic is represented by a probability distribution over the entire vocabulary, and created a topic model with the number of topics derived through the elbow rule from model coherence scores. in the initial selected set of tweets, the topic analysis revealed four dominant topics, signifying the entities twitter users yearn for. these topic clusters are: (1) hope: hope, find, story; (2) transition to online: online, call, show, work; (3) life and death: life, die, religion, helpless, crisis; (4) activities and people missed: summer, friend, family, future. as the tweets are short, we assume each tweet only contain one topic and we annotate the tweet with the most dominant topic, i.e., the topic with the highest percentage weightage. this topic information per tweet is used to augment the emotion topic hidden markov model, drawing on the fact that tweet topics can significantly influence expressed emotions @xcite . the final dataset yielded a topic cluster of 10 topics, presented in ## predicting emotion dynamics we describe the method and results in predicting emotion dynamics through a proposed emotion topic hidden markov model (et-hmm) framework. we evaluate our et-hmm against naive bayesian and support vector classifier models. emotion topic hidden markov model. we model user emotion state transitions using a probabilistic sequence model: the markov model @xcite . in the markov model, the exhibition of the next emotional state is based on the current state, defined by the transition probabilities of moving from one state to another across neighbouring time periods. we augment the user transition markov model with discrete distributions of dominant topics as hidden states, constructing an emotion topic hidden markov model (et-hmm) foot_0 , which reflects the importance of topics on emotions, to explore user feelings when they tweet on a particular topic. figure 5 shows the result of the et-hmm constructed using the pomegranate python library @xcite . we observe that the emotions anger and neutral has the highest initial probability, meaning when a random user first tweets, he is likely to post a tweet with these emotions during the pandemic. with the exception of fear, all emotions are equally as likely to be found at the last tweet of a trail. if a user is in an angry state, the probability of staying in the same state in subsequent tweets is higher than other emotional states. emotion prediction with et-hmm. we perform a predictive task using our et-hmm model. given a user's past emotion dynamics trail consisting of a sequence of topics and emotions, we attempt to predict his subsequent emotion dynamic. we construct an emotion dynamic trail for each user using the dominant topic and emotions for each tweet. we then remove the final tweet's emotion dynamics from the trail, to be used as the test data point for the sequence. using the viterbi algorithm, we predict the emotion dynamic of each user, given his emotion dynamic trail. we select the states that have the maximum probability of occurring given the trail of previous topics and emotions. ## discussion in this study, we analyzed emotion dynamics in twitter during the covid-19 pandemic, characterizing topics, emotions and their transitions. in our dataset, we observe a sharp increase in the number of tweets collected from the week of march 22 to march 29. during this week, at least eight states in the us have issued stay-at-home orders, and many other countries begin movement restrictions, which might cause the great outpouring of yearning expressed on twitter as people adjust to staying and working at home. another week of note is april 19 to may 3, where countries like spain, italy and malaysia extend stay-at-home orders due to persistent rise in cases. we postulate the decrease in the number of tweets may be due to adjustments to new routines and people have leveraged digital technologies to overcome the yearning for family ## bye, i ĺl miss you babe: conclusion in this paper, we characterised twitter users' emotion dynamics during the covid-19 pandemic in march 2020 by performing longitudinal user studies profiling topics discussed and the emotions exhibited. during this time period, disgust and anger are the most commonly expressed emotions, and that user emotion transitions tend to exhibit four main "mood swing" profiles. we construct an emotion topic hidden markov model to reflect the importance of tweet content on emotional state, presenting a model that outperforms baselines. we hope our work sheds light on user emotions during a pandemic, and will inspire future directions in monitoring emotional states during isolation events, providing a means to identify high-risk individuals to offer emotional support through trying times. for future work, we will consider tweet geographical location information in our analysis. for example, countries have different developments of the covid-19 pandemic, and the varying health situation may elicit different emotions of twitter users living in the country. geolocation analysis may explain the changes in the users' emotional states at certain locations at certain times. we will also consider modeling emojis and other multimedia content in our topic and emotion analysis.