# Exploring Social Media for Early Detection of Depression in COVID-19
Patients

Jiageng Wu  Zhejiang UniversityHangzhouChina<EMAIL_ADDRESS>,  Xian Wu
Tencent Jarvis La bBeijingChina<EMAIL_ADDRESS>,  Yining Hua  Harvard
UniversityBostonUSA<EMAIL_ADDRESS>,  Shixu Lin  Zhejiang
UniversityHangzhouChina<EMAIL_ADDRESS>,  Yefeng Zheng  Tencent Jarvis
LabHong KongChina<EMAIL_ADDRESS>and  Jie Yang  Zhejiang
UniversityHangzhouChina<EMAIL_ADDRESS>
(2023)

###### Abstract.

The COVID-19 pandemic has caused substantial damage to global health. Even
though three years have passed, the world continues to struggle with the
virus. Concerns are growing about the impact of COVID-19 on the mental health
of infected individuals, who are more likely to experience depression, which
can have long-lasting consequences for both the affected individuals and the
world. Detection and intervention at an early stage can reduce the risk of
depression in COVID-19 patients. In this paper, we investigated the
relationship between COVID-19 infection and depression through social media
analysis. Firstly, we managed a dataset of COVID-19 patients that contains
information about their social media activity both before and after infection.
Secondly, We conducted an extensive analysis of this dataset to investigate
the characteristic of COVID-19 patients with a higher risk of depression.
Thirdly, we proposed a deep neural network for early prediction of depression
risk. This model considers daily mood swings as a psychiatric signal and
incorporates textual and emotional characteristics via knowledge distillation.
Experimental results demonstrate that our proposed framework outperforms
baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC
of 0.8116. Our model has the potential to enable public health organizations
to initiate prompt intervention with high-risk patients.

Natural language processing, Social media, Depression detection

††journalyear: 2023††copyright: acmlicensed††conference: Proceedings of the
ACM Web Conference 2023; May 1–5, 2023; Austin, TX, USA††booktitle:
Proceedings of the ACM Web Conference 2023 (WWW ’23), May 1–5, 2023, Austin,
TX, USA††price: 15.00††doi: 10.1145/3543507.3583867††isbn:
978-1-4503-9416-1/23/04††ccs: Information systems Data mining††ccs: Applied
computing Health informatics

##  1\. Introduction

Since the outbreak of COVID-19 in 2020, this global pandemic has caused 625
million infections and 6.57 million deaths.111https://covid19.who.int/ Even
though it has been three years, COVID-19 has not been eradicated worldwide.
The rapid spread of the pandemic and the resulting economic downturn have
exacerbated widespread anxiety, confusion, emotional isolation, and panic
(Pfefferbaum and North, 2020). According to Global Burden Disease (2020) study
(Santomauro et al., 2021), the COVID-19 pandemic has caused nearly a 27.6%
increase in depression and a 25.6% increase in anxiety worldwide.

Depression, which affects an estimated 3.8% of the world’s
population222https://www.who.int/news-room/fact-sheets/detail/depression, is
now the leading cause of mental health-related disease burden globally
(Herrman et al., 2019). Depression causes persistent feelings of sadness that
negatively affect how individuals feel, think, and act. In severe cases,
depression can lead to suicide (Patel et al., 2016). Approximately 5% of
depressed adolescents will commit suicide (Harrington and Clark, 1998).
However, depression is preventable and treatable (for Disease Control et al.,
2010), and the sooner it is treated, the better the outcome (Harrington and
Clark, 1998). Despite a 41% increase in the burden of mental disorders over
the past two decades (Patel et al., 2016), mental health remains one of the
most neglected yet crucial development issues. In many low- and middle-income
countries (LIMCs), there are fewer than one mental health worker for every
100,000 people, and more than 75% of people do not receive treatment
(Santomauro et al., 2021).2

Figure 1. Tweet Timeline of a COVID-19 Patient with Depression. Tweet $t_{i}$
represents the patient’s mention of their COVID-19 diagnosis, while tweet
$t_{j}$ represents their mention of becoming depressed. To perform early
prediction of depression risk, we selected a set of tweets
$T_{S}=\\{t_{1},t_{2},\ldots,t_{s}\\}$ posted at least two weeks prior to
$t_{j}$ to predict the likelihood of the patient developing depression. (Note
that all raw tweets included in this paper have been rephrased for
desensitization and brevity.)

early detection of depression

To alleviate the depression crisis caused by COVID-19, it is crucial to detect
depressed patients at an early stage so that they can receive prompt treatment
(Picardi et al., 2016). Nonetheless, social stigma and self-stigma have
emerged as significant barriers to treatment (Latalova et al., 2014; Barney et
al., 2006). Despite the fact that depression can result in social withdrawal
and isolation, many affected individuals attempt to disclose their experiences
on social media due to the virtuality and privacy of social identity (Luo and
Hancock, 2020; Naslund et al., 2014). Moreover, online communities provide a
hospitable environment that enables individuals to connect with others who
face comparable challenges (De Choudhury and De, 2014a). After the outbreak of
COVID-19, the use of social media platforms has increased by 61% as people
rely on them to stay in touch with others (Saha et al., 2020). As more
individuals with depression tend to self-disclose and seek assistance on
social media, these platforms provide a rich ecosystem for studying the
manifestation and characteristics of depression.

This paper aims to develop a social media-based depression early detection
model among COVID-19 patients. Using a knowledge distillation framework, our
proposed model combines the longitudinal contextual information from Twitter
posts and the daily emotional status of COVID-19 patients to predict their
risk of depression. The contributions of this work are as follows:

Firstly, we managed a dataset (DepCOV) comprising 10,656 Twitter users. It
includes users at risk for depression following a COVID-19 diagnosis and a
control group. We collected the date of COVID-19 infection, pre-infection
posts, and post-infection posts for each patient in the dataset.

Secondly, we conducted in-depth experiments and data analysis to investigate
the relationship between COVID-19 infection and depression. Our analysis
focuses on identifying linguistic differences between depressed users and
controls, as well as pre-and post-infection differences.

Thirdly, we developed an early depression risk detection model for COVID-19
patients. Figure 1 illustrates the historical tweets of a COVID-19 patient.
The patient was infected around the time of tweet $t_{i}$ and developed
depression signals around tweet $t_{j}$. To perform early prediction of
depression risk, we selected tweets posted at least two weeks prior to
$t_{j}$. Given the significant negative impact of COVID-19 on mood (Campos et
al., 2021; Robbins et al., 2022), we used mood swings as a potential
diagnostic signal for depression detection. Our proposed Mood2Content model
integrates both textual and emotional features through knowledge distillation
to make predictions. Experiment results show that Mood2Content outperforms
other competitive baselines, achieving high performance with an AUROC of
0.9317 and AUPRC of 0.8116.

##  2\. RELATED WORK

###  2.1. Depression Detection in Social Media

Unlike the conventional machine learning task in other fields that are
supported by extensive and high-quality datasets with gold-standard diagnoses,
the myriad of privacy and ethical concerns of mental disorders have limited
the accessibility of datasets with clinically validated diagnostic
information. Consequently, many researchers have devoted themselves to
constructing reliable datasets to support various tasks. The
annotation/development schemes of a dataset are mainly based on affiliation
behaviors, self-reports, and expert/external validation (see more details in
(Chancellor and De Choudhury, 2020; Ernala et al., 2019)). The most ideal
datasets are curated by the third scheme, which introduces the experts’
examination (Birnbaum et al., 2017) or incorporate electronic healthcare
records (Eichstaedt et al., 2018), but its effort- and time-consuming nature
limit its scale, diversity, and accessibility (Ernala et al., 2019).
Therefore, the first two methods are the most popular and practical schemes.
The first strategy operationalizes hashtags, account following, and community
participation related to psychiatric resources as interested signals, such as
followers of psychiatrist account (McManus et al., 2015), posts in depression
forum (De Choudhury and De, 2014b; Shen and Rudzicz, 2017; Wolohan et al.,
2018). The third scheme identifies the interested person according to their
self-disclosure in social media, such as the matching pattern for feelings or
diagnoses of mental disorders (e.g., ”I was diagnosed with depression”). For
example, (Coppersmith et al., 2014) adopted the regular expression of
diagnosed pattern to seek persons with mental disorders in Tweet. Since then,
more similar datasets have been proposed, such as RSDD (Yates et al., 2017),
SMHD (Cohan et al., 2018), eRisk (Losada et al., 2019), and have flourished
related workshops, such as CLPsych (Coppersmith et al., 2015) and eRisk
(Losada et al., 2017). Beyond these, Kelly and Gillan recruited participants
who self-reported depressive episodes through an online worker platform
(Kelley and Gillan, 2022; Kelley et al., 2022).

Research about mental health based on social media mainly focused on the
detection model and the potential indications of mental disorders. For model
development, the classical paradigm is the combination of feature extraction
and classifier, such as linguist features with logistic regression
(Coppersmith et al., 2014). The common feature extraction methods include TF-
IDF, word embedding (Trotzek et al., 2018), LIWC (Linguistic Inquiry and Word
Count) (Tausczik and Pennebaker, 2010), and LDA (Latent Dirichlet Allocation)
(Blei et al., 2003). Currently, more research has gradually used deep learning
models to represent posts, including convolution neural network (CNN) (Cohan
et al., 2018), recurrent neural network (RNN) (Ive et al., 2018), long short-
term memory neural network (LSTM) (Cao et al., 2019) and Transformer (Zogan et
al., 2021). Meanwhile, (Gui et al., 2019) and (An et al., 2020) cooperated
with the text and image by the multi-modality model. Especially, several
research introduced the attention-based approach to improve model
interpretability and generalizability (Burdisso et al., 2019), such as
hierarchical attention networks (HAN) (Yang et al., 2016). And, recent studies
cooperated with the psychiatric scale of clinical diagnosis to guide
depression detection (Nguyen et al., 2022; Zhang et al., 2022).

There is a growing interest in exploring the potential of social media for
depression diagnosis, including linguistic characteristics (Harrigian and
Dredze, 2022; Eichstaedt et al., 2018) and social behavior (Lin et al., 2017).
Studies have shown that LIWC, LDA, and text clustering can be used to examine
linguistic differences between individuals with schizophrenia and healthy
controls (Mitchell et al., 2015). Trotzek et al. (Trotzek et al., 2018) built
a logistic regression classifier by integrating readability and emotion
features into user-level linguistic metadata and further improved it with a
CNN-based model. The work in (Shen et al., 2017) involved depression detection
using multi-modality features such as social network features, user profile
features, visual features, emotion features, topic-level features, and domain-
specific features. Yang et al. (Yang et al., 2022) extracted mental state
knowledge and infused it into a GRU model to explicitly model the mental
states of the speaker. Kelley et al. (Kelley and Gillan, 2022) constructed
personalized, within-subject networks based on depression-related linguistic
features from LIWC and discovered a positive correlation between overall
network connectivity and depression severity. The negative mood, a typical
symptom of depression, has also been extensively studied in the context of
social media posts, with the majority of research concentrating on content
analysis or the extraction of hand-crafted features using lexicons or rules
(Wang et al., 2013; Chiong et al., 2021).

As a global health crisis, COVID-19 has received significant attention on
social media. On the basis of large-scale social media data, there has been an
abundance of research on COVID-19 (Tsao et al., 2021), including thematic
analysis (Li et al., 2022b), symptom identification (Xue et al., 2020; Wu et
al., 2022), and public perception analysis (Boon-Itt et al., 2020; Li et al.,
2022a). However, research modeling the relationship between depression and
COVID-19 is scarce. This paper represents, to the best of our knowledge, the
first attempt to predict early the depression risk of COVID-19 patients.

###  2.2. Research on Knowledge Distillation

Large-scale deep learning models have limited practical applications due to
their computational complexity and storage requirements. Knowledge
distillation (KD) is a solution to this issue, as it enables the distillation
of a large model into a smaller model with a relatively low reduction in
performance (Gou et al., 2021). The student model of KD is synchronously
guided by the distillation loss that reflects the gap between the student
model and teacher model, and the task loss that measures the prediction errors
of the student model (Hinton et al., 2015). Different KD strategies define
distillation loss differently. Distilled BiLSTM (Tang et al., 2019) used the
MSE loss between the output of the teacher model and the student model as the
distillation loss. BERT-PKD (Sun et al., 2019) extracted information from
intermediate layers and computed the MSE loss. DistillBERT (Sanh et al., 2019)
and TinyBERT (Jiao et al., 2019) guided the student model in the pre-training
stage. MiniLM (Wang et al., 2020) further distilled the self-attention
distributions and value relations of the teacher’s last Transformer layer to
guide the student model training, making it effective and generative for
student models.

##  3\. METHODOLOGY

Figure 2. Framework of the Mood2Content model which includes a Content Encoder
and a Mood Encoder, both encoders accept daily aggregated tweets as input and
target to predict the depression risk at an early stage.

###  3.1. Problem Formulation

This study uses Twitter as the major social platform to detect depression and
predict early-stage risks. Given a user $u$, we can acquire his historical
tweets, such as posts and comments, which contain abundant information about
personal experiences and feelings. We denote all tweets acquired from $u$ with
$T=\\{t_{1},t_{2},\ldots,t_{N}\\}$, where $N$ is the total number of tweets.
We also denote the first tweet mentioning being infected by COVID-19 as
$t_{i}$ and the first tweet emitting depression signals as $t_{j}$. In this
paper, we aim to detect potential depression before users explicitly express
depressive feelings and after they get COVID-19. Therefore, we focus on cases
where $t_{j}$ is posted after $t_{i}$. For early detection, we further limit
our study range to tweets posted at least two weeks before $t_{j}$.
Consequently, the early depression risk prediction problem can be formulated
as a binary classification problem on predicting a future depression label $y$
for the user $u$ using the subset $T_{s}=\\{t_{m},t_{m+1},\ldots,t_{n}\\}$
from $T$, where $t_{i}<t_{m}<t_{n}<t_{j}$ on timescale.

###  3.2. Feature Extraction

####  3.2.1. COVID-19 Infection Time Extraction

This subsection presents the extraction of $t_{i}$, i.e., the first tweet
where the user self-reported a COVID-19 diagnosis. We identify self-reported
COVID-19 tweets through the following steps: 1) use keywords to filter tweets
that contain specific expression phrases, such as “get COVID”, or “test
positive”. Then, we use dependency parsing (supported by Stanza (Qi et al.,
2020)) and rule-based approaches (such as negation detection) to determine the
subject of infection. The first tweet with the user as the infection subject
is associated with a timestamp, but this timestamp does not necessarily
represent the user’s infection time $t_{i}$. Therefore, we further applied the
regular expression to extract time information in the tweet to determine the
user’s infection time $t_{i}$. More details and related resources on dataset
construction can be found in the code repository.333https://github.com/Dragon-
Wu/DepCov-WWW2023

####  3.2.2. Depression Time Extraction

This subsection presents the extraction of $t_{j}$, i.e., the first tweet
where the user expressed depressed feelings after COVID-19 infection.
Following (Cohan et al., 2018; Nguyen et al., 2022), we define self-reported
depression as tweets that mention depression conditions and first-person
pronouns within a short lexical distance. Based on official psychiatric
resources444https://www.mayoclinic.org/diseases-
conditions/depression/,555https://www.who.int/news-room/fact-
sheets/detail/depression,666https://www.nimh.nih.gov/health/topics/depression,
we curate a comprehensive lexicon of depression conditions. The lexicon
contains various expressions of depressive disorders (e.g., major depression
disorder, dysthymia), the status of extreme depression mood (e.g., miserable,
hopeless), and typical symptoms of depression (e.g., suicide, severe mood
swings). In addition, we also add colloquial expressions. With this lexicon
and high-precision regular expression, we extract tweets with depressive
signals and remove tweets with ambiguity, non-self-report, and negation.
Manual validation on a random sample of 200 tweets shows an accuracy of 91.0%.

####  3.2.3. Aggregation of Daily Tweet

After identifying the infection time $t_{i}$ and depression tweet $t_{j}$ were
identified, we selected all tweets that were posted before $t_{j}$ (e.g., two
weeks) and denoted them as $T_{s}={t_{m},t_{m+1},\ldots,t_{n}}$. The objective
was to extract features from $Ts$ in order to predict whether this user would
develop depression in the near future. Due to Twitter’s character limit,
tweets were typically brief, making semantic and sentiment analysis difficult
and resulting in frequent mood swings. To address this issue, we condensed the
historical tweets $T_{s}$ into daily tweets $D={d_{1},d_{2},...,d_{M}}$ and
sorted them in reverse order, where $d_{i}$ represented all tweets generated
on the $i$th day. While everyone’s mental state fluctuates over time,
including those of depressed patients, using tweets posted a long time ago may
not have accurately reflected their current mental state and could have led to
inaccurate predictions. To improve efficiency and focus on the current state
of the user, we truncated historical posts after four weeks, enabling online
and timely detection. Consequently, the maximum number of elements in $D$ was
at most 28 (4 $\times$ 7). If there are not 28 daily tweets from the past four
weeks, the latest daily tweets from historical tweets will be collected to
meet the sliding window.

####  3.2.4. Tweet Representation

After merging daily tweets into $D=\\{d_{1},d_{2},...,d_{M}\\}$, we adopt BERT
(Devlin et al., 2018) as the textual encoder to represent each $d_{j}$ in $D$.
Here we use the COVID-Twitter-BERT-v2 (CTB) (Müller et al., 2020), a BERT-
large-uncased model that has been incrementally pre-trained on large-scale
COVID-19-related tweets. In recognition of the important role of emotional
information in depression detection, we also develop a Mood Encoder to capture
the emotional context of tweets. To enhance its capability, we further pre-
train the CTB model on three sentiment-related tasks: sentiment classification
(Barbieri et al., 2020; Rosenthal et al., 2017), emotion recognition (Mohammad
et al., 2018), and targeted sentiment analysis (Zhou et al., 2022). These
three tasks yield three optimized models based on CTB, we denote them with
CTB-St, CTB-Emo, and CTB-Tsa, respectively.

The CTB-St and CTB-Emo models were fine-tuned using the SemEval 2017-Sentiment
Analysis in Twitter (Rosenthal et al., 2017) and SemEval 2018 - Emotion
Recognition (Mohammad et al., 2018) datasets, respectively. CTB-St categorizes
the overall sentiment of tweets into negative, neutral, and positive, while
CTB-Emo infers the emotional state of a tweet (anger, joy, sadness, optimism).
Both models were fine-tuned with a basic BERT setting, which involves mean-
pooling the embeddings of the last hidden state of CTB and inputting it into a
linear classifier. The third task, TSA (Targeted Sentiment Analysis), is a
fine-grained sentiment analysis aimed at inferring user sentiment toward
targeted entities (negative, neutral, and positive). The CTB-Tsa model was
fine-tuned on the METS-CoV dataset (Zhou et al., 2022), which contains
COVID-19 related tweets, using the BERT-SPC model setting (Devlin et al.,
2018).

For each daily aggregated tweet $d_{j}=\\{w_{1},w_{2},...,w_{N_{j}}\\}$ of
user $u$, we adopt the mean-pooling of the last hidden state of BERT model as
the tweet representation:

(1)
$c_{j}=Content_{Encoder}(d_{j})=\frac{1}{N_{j}}\sum^{N_{j}}_{l=1}BERT_{|LAST|}(w_{1},w_{2},...,w_{l})$

where $c_{j}$ refers to content representation of $d_{j}$.

(2) $\displaystyle m_{j}$ $\displaystyle=Mood_{Encoder}(d_{j})$

where $m_{j}$ refers to the mood representation of $d_{j}$ and
$Mood_{Encoder}$ can be one of CTB-St, CTB-Emo and CTB-Tsa.

###  3.3. Mood2Content Model

As shown in Figure 2, we propose a novel framework Mood2Content that
cooperates with both the content representation and mood representation to
conduct early detection of depression. Given a user $u$ to with daily merged
tweets $D=\\{d_{1},d_{2},...,d_{M}\\}$, we can use the content encoder and the
mood encoder to acquire the corresponding representation
$C=\\{c_{1},c_{2},\ldots,c_{M}\\}$ and $M=\\{m_{1},m_{2},\ldots,m_{M}\\}$.
Then we generate the embedding $x$ of user $u$ based on $C$ and $M$.

The set $D$ contains the merged daily posts sorted in reverse chronological
order, such ranking information needed to be included in modeling. This is
because the most recent tweets record the current status of this user which is
more informative for future depression risk prediction. As a result, we add
the position information to the content representation $c_{j}$ of each $d_{j}$
in $D$.

(3) $c^{\prime}_{j}=Concatenation(c_{j},pos_{j})$

where $pos_{j}$ is a hard position embedding denoting the day gap between the
$j$th day and now, emphasizing the timeline information.

After updating content representation with position information, we acquire
the user representation $x$ with a user encoder that consists of Transformer
and self-attention layer. Transformer enables $c^{\prime}_{j}$ to utilize the
information from other daily tweets. $s_{j}$ is the $j$th embedding of the
last hidden state of Transformer:

(4) $s_{j}=User_{Encoder|LAST|}(C^{\prime},j)$

A self-attention layer is used to generate the weighted sum of all $s_{i}$:

(5) $\alpha_{j}=\frac{exp(Ws_{j}+b)}{\sum^{M}_{k=1}exp(Ws_{k}+b)}$ (6)
$x=\sum_{j=0}^{M}\alpha_{j}s_{j}$

where $W$ are learnable parameters. Then, the user representation $x$ is the
input of the classifier head $F$ (linear layer) to predict the depression risk
$p$.

(7) $p=SIGMOID(W_{F}\cdot x+b_{F})$

where $W_{F}$ are learnable parameters. Therefore, the model can be trained
with the loss function of depression prediction $\mathcal{L}_{clf}$ :

(8) $\mathcal{L}_{clf}=y\cdot logp+(1-y)\cdot log(1-p)$

To integrate the mood representation into depression risk prediction, inspired
by knowledge distillation, we guide the content encoder to align with the mood
representation. In detail, we first acquire the mood representation
$M=\\{m_{1},m_{2},\ldots,m_{M}\\}$ in Eq.(2). Then, this mood encoder will be
frozen in depression detection and no longer update model weights. We
introduce $\mathcal{L}_{distill}$ as a distance measure between mood vector
$M_{i}$ and content vector $C_{i}$, which guides the content encoder to reach
a trade-off between feature fusion and model classification:

(9) $\mathcal{L}_{distill}=\lvert\lvert M-C\lvert\lvert^{2}_{2}$

Therefore, the Mood2Content model is optimized towards both mood distillation
and prediction error reduction. The overall loss of model can be formulated as
a weighted sum of $\mathcal{L}_{clf}$ and $\mathcal{L}_{distill}$:

(10)
$\begin{split}\mathcal{L}&=\alpha\cdot\mathcal{L}_{clf}+(1-\alpha)\cdot\mathcal{L}_{distill}\\\
&=\alpha\cdot[y\cdot logp+(1-y)\cdot log(1-p)]+(1-\alpha)\cdot\lvert\lvert
M-C\lvert\lvert^{2}_{2}\end{split}$

where $\alpha$ is an adjustable factor that can emphasize feature fusion or
classification.

##  4\. EXPERIMENTS

###  4.1. Dataset

We select original English tweets related to COVID-19 using unique tweet
identifiers (tweet ID) from a widely used open-source COVID-19 tweet database
(Chen et al., 2020; Lopez and Gallemore, 2021). These tweets were identified
by Twitter’s trending topics and keywords associated with COVID-19, such as
COVID-19 and SARS-COV-2. We first download 471,553,966 target tweets across 27
months, from February 1st, 2020, to April 30th, 2022, using Twitter’s
Application Programming Interface (API). After the identification of COVID-19
patients, we further collect retrospective tweets between January 1st, 2020,
and December 31st, 2021 from each infected user for further analysis and
modeling.

Due to the mental disease problems brought by COVID-19, we presume that there
are many vulnerable persons who may present depression risk after COVID-19
diagnosis. We split the entire user set into two groups according to their
quantity and the corresponding timestamp of depression tweets: 1) the first
group is the treatment group which includes users emitting depression signals
after suffering COVID-19. We require users in this group to have posted more
than three depression tweets and the first of which was posted at least two
weeks after their COVID-19 diagnosis. In addition, these users never post a
depression tweet before COVID-19. Particularly, we set a window period of two-
week, a widely used time window in the diagnosis of mental disorders, between
COVID-19 diagnosis and the emergence of depression risk and the subsequent
modeling and analysis merely utilize their tweets before it; 2) the second
group is the control group which includes users who don’t mention depression
both before and after COVID-19 infection. For each user in the first group, we
select 5 users with a similar quantity of tweets and add them to the second
group. Besides, all eligible users must contain more than 25 tweets both
before and after the COVID-19 diagnosis respectively, $\geq 75\%$ of which are
written in English.

In this manner, we build a dataset of COVID-19 patients with depression
signals and name it the DepCOV dataset. DepCOV consists of 1,776 depression
cases (positive) and 8,880 controls (negative), with 10,488,061 tweets. For
model development and evaluation, We split the DepCOV into the training set,
validation set, and testing set with the proportion of 7:1:2.

As the overall statistic of DepCOV is shown in Table 1, the depressed person
among COVID-19 patients posted more tweets than the controls, and this has
been further enhanced after they got COVID-19. Besides, the depressed users in
the DepCOV have an average of 5.27 depression tweets and the time between
their COVID-19 diagnosis and depression was an average of 59.41 days.

###  4.2. Settings

Table 1. The statistics of the proposed dataset DepCOV. (Both include COVID-19
patients who will or will not get depressed in two weeks)

Statistics | Depression (n=1,776) | Controls (n=8,880) |  DepCOV (n=10,656)
---|---|---|---
(Mean) | Before COV | COV to Dep | Before COV | After COV |  Overall
Tweets Count | 583.74 | 383.44 | 586.87 | 400.78 | 492.12
Days Count | 109.57 | 59.01 | 149.40 | 108.70 | 121.59
Tweet length | 23.21 | 23.95 | 21.44 | 21.47 | 21.81
Tweet per day | 5.33 | 6.50 | 3.93 | 3.69 | 4.16
Daily Tweet length | 113.64 | 135.88 | 78.64 | 75.86 | 85.17

To evaluate the model performance objectively, models of the same type have
exactly the same parameters. The max number of training epochs is 10 and the
patience of early stop is 10. The training batch size is 32 and the learning
rate is 5e-5 with the cosine scheduler with warm-up. The $\alpha$ of
Mood2Content model is 0.5, which yields a balance between distillation loss
and classification loss.

To avoid the influence of randomness, we run each model with 3 different seeds
(42, 52, 62) and report the average performance. For the practical
availability and generalizability, we adopt the area under the receiver
operating characteristic curve (AUROC) and the precision-recall curve (AUPRC)
instead of accuracy or F1-score, which are widely used in such tasks but set a
hard threshold of 0.5. AUROC and AUPRC can more comprehensively evaluate the
model performance regardless of any threshold, enabling more aggressive or
conservative interventions for persons at depression risk.

###  4.3. Analysis

####  4.3.1. Linguistic discrepancy

To analyze content differences, we compared psycholinguistic characteristics
between COVID-19-infected patients who developed depression and those who did
not, as well as between tweets posted by depressed patients prior to and after
their COVID-19 diagnosis. We utilized the LIWC lexicon, a psychometrically
validated mapping of words to psychological concepts that had been widely
applied to the analysis of mental health in social media text (Tausczik and
Pennebaker, 2010; Coppersmith et al., 2014). We conducted Chi-square tests on
each characteristic and determined its odds ratios (ORs). Table 2 displayed
the characteristics with the ten highest and ten lowest odds ratios, with
p-values for each result ¡0.0001.

Compared to pre-COVID-19 diagnosis, depressed individuals used fewer words
associated with recreation (leisure, positive emotion, friends, and motion)
and more words associated with sexuality, health, risk, negation, and anger,
indicating a change in their lifestyle and concerns (Ustun, 2021). Similarly,
depressed individuals expressed fewer positive words (accomplishment, reward,
power, and leisure) than non-depressed individuals. Specifically, and in
accordance with clinical or social media studies, we observed that depressed
individuals tended to use more first-person pronouns than controls, indicating
an increase in self-focused attention (Kelley et al., 2022; Holtzman et al.,
2017). In addition, an increase in female- and family-related words may
reflect a description of familial affection.

Table 2. Discrepancy of psycholinguistic feature.
(The odds ratios (ORs) quantify the linguistic disparities between depression
and controls, as well as between the pre- and post-COVID phases of the
depression. All p ¡ 0.0001)

After COVID-19 VS Before COVID-19 (Depression) | Depression VS Controls
---|---
Category | OR | Category | OR | Category | OR | Category | OR
Leisure | 0.92 | Sexual | 1.08 | Money | 0.85 | I | 1.26
Ingest | 0.94 | Health | 1.04 | You | 0.88 | Female | 1.17
Nonfluencies | 0.96 | Risk | 1.04 | Achievement | 0.93 | Family | 1.10
See | 0.96 | They | 1.04 | Work | 0.93 | Ingest | 1.10
Home | 0.97 | Money | 1.04 | Death | 0.93 | Filler Words | 1.10
Affiliation | 0.97 | Causal | 1.03 | Reward | 0.93 | Anxiety | 1.08
Positive Emotions | 0.97 | SheHe | 1.03 | Power | 0.94 | Insight | 1.08
Perceptual Processes | 0.97 | Negations | 1.02 | Leisure | 0.94 | Feel | 1.07
Friends | 0.97 | Insight | 1.02 | We | 0.96 | Assent | 1.06
Motion | 0.97 | Anger | 1.02 | Drives | 0.96 | Religion | 1.05

####  4.3.2. Content analysis of Depression tweet

To shed light on the potential underlying causes of depression, we analyzed
the content of tweets pertaining to depression. The tweets were filtered for
depression-related conditions, and Latent Dirichlet Allocation (LDA) was used
to identify the primary concerns of depressed individuals and determine what
factors may have contributed to their depressive moods. The number of topics
was limited to between ten and 200, and the optimal model was chosen based on
its coherence and complexity. Table 3 displays the leading ten topics and
their top 20 words for the best model, which had 125 topics. Our analysis
revealed that the sources or targets of negative emotions were frequently
associated with the ongoing pandemic, such as the lockdown, government,
treatments, and the disease itself. In addition, additional topics centered on
the participants’ emotions and feelings, including depression, encouragement,
and complaints.

Table 3. The most concerned topics of depression tweets (Top-10 words of
top-10 topics)

Topic | Keywords
---|---
Lockdown |  absolutely, outside, door, figure, stream, breathe, pressure, air, strange, day
Government |  place, government, wonder, explain, bunch, result, people, fix, citizen, believing
Depression |  attack, panic, panic_attack, piece, awful, reminds, time, victim, forced, failure
Policy |  American, fast, freedom, middle, exist, accept, overwhelming, hero, military, ocd
Encouragement |  went, fall, strong, time, happens, option, praying, counseling, stay, stay_strong
Complaint |  mind, fear, fact, past, win, space, city, committing, medicine, the_fact
Treatment |  second, heard, happened, sadness, pill, smoking, xanax, intense, recovery, nausea
Disease |  heart, ask, important, ill, beat, present, reality, pm, heart_attack, alcohol

###  4.4. Early Depression Detection

####  4.4.1. Baselines

To fully evaluate the performance of different models in our experiment
settings, we constructed several baselines which range from statistical NLP
models to deep learning models.

LIWC+LR: As users’ language characteristic can reveal their psychological
state, the first baseline is LIWC+LR which extracts the psycholinguistic
features of merged historical tweets by LIWC (Tausczik and Pennebaker, 2010)
and predict the depression risk by logistic regression classifier.

TF-IDF+XGBoost: It adopts TF-IDF weighted features of word and character
n-grams and the popular machine learning model XGBoost (Chen et al., 2015),
which is also extensively used in similar tasks (Wolohan et al., 2018).

HAN: The simple concatenation of all tweets in the absence of temporal
information may easily lead to the crucial clues lost in large-scale corpus
(Zhang et al., 2022). Therefore, we select the representative HAN (Yang et
al., 2016) as a deep learning baseline, which conducts depression prediction
with a hierarchical attention neural network. HAN obtains each tweet $t_{j}$
representation with bidirectional GRU and further encodes all tweet
representations into user presentation with an attention mechanism to conduct
the final prediction. To improve model performance, we select the 500, 1000,
2000 latest tweets, and 4-weeks’ daily tweets as model input to develop
baseline model HAN-500, HAN-1000, HAN-2000 and HAN-Daily, respectively. HAN-
Daily(BERT) takes daily tweets as input and replaces the BiGRU with CTB. On
the basis of HAN(BERT), HAN-User adds the user encoder of Mood2Content to
encode tweet representation.

Mood&Content: It directly concatenates the mood representation and the content
representation of daily tweets to generate the combined representation, which
acts as the tweet representation to generate user representation for
subsequent prediction.

####  4.4.2. Results

As the model performance shown in Table 5 and Table 6, the proposed
Mood2Content outperforms other models in early depression detection. Among the
baseline models, the HAN-Daily achieved the highest performance, indicating
that the recent and aggregated daily tweet can promote the model to seize the
recent changes in users’ mental status. Meanwhile, the inclusion of more
historical tweets did not always improve performance by comparing the results
of HAN-500, HAN-1000, and HAN-2000.

For the advanced model, extensive experiments were conducted to demonstrate
the performance of the different strategies and to investigate the effects of
different components simultaneously:

Effect of Tweet Encoder Owing to the selection and aggregation of daily
tweets, a large-scale pre-trained language model can be used as a tweet
encoder instead of conventionally shallow CNN or GRU (Yates et al., 2017). The
HAN(BERT) model achieved an improvement of 0.1333 in AUPRC and 0.0493 in AUROC
at most than the original HAN with BiGRU. Meanwhile, other models with BERT-
based tweet encoders all achieved good performance.

Effect of User Encoder Compared with HAN(BERT), the HAN-User was improved by
introducing a user encoder, which consists of position embedding, Transformer,
and a self-attention layer to further encode tweet representation. The
cooperation of position embedding and Transformer enables the user encoder can
capture the longitudinal information and the final self-attention layer
improves the model interpretability.

Table 4. Performance of fine-tuned CTB on sentiment tasks.
(CTB-st is fine-tuned on sentiment classification task, CTB-Emo is fine-tuned
on emotion recognition task and CTB-Tsa is fine-tuned on targeted sentiment
analysis task)

Model | Acc | Recall | F1
---|---|---|---
CTB-St | 0.7183 | 0.7260 | 0.7173
CTB-Emo | 0.8294 | 0.8034 | 0.7974
CTB-Tsa | 76.29 | 0.6738 | 0.7003

  * •

(The bold means the recommended metric)

Effect of Emotional Signal With The result of HAN(BERT) and HAN-User
demonstrated that emotional BERT can be also used as a tweet encoder, and CTB-
Tsa resulted in better performance than general BERT in them. Notably, such
strategy acts as modeling daily mood swing as the potential diagnostic signal
for depression detection, which is consistent with psychiatric studies (Lane
and Terry, 2000; Krishnan and Nestler, 2010) and the clinical practice
(Kroenke et al., 2001; Beck et al., 1987).

Effect of Different Mood Encoder The results of fine-tuned models are shown in
Table 4, which all nearly reached the reported SOTA performance. We examined
the improvement brought by the different mood encoders. All the mood coders
performed about the same, and the CTB-Tsa achieved the best result in 3/4
models. This could be due to the specific COVID-Twitter dataset, or the fine-
grained sentiment analysis could capture more mood information.

Effect of Mood Distillation As shown in Table 6, the proposed Mood2Content
yielded the highest performance with an AUPRC of 0.8116 and an AUROC of
0.9317. Compared to HAN-User, which relied solely on the content or emotional
information, Mood2Content improved by an average of 0.0548 in AUPRC and 0.0160
in AUROC. However, Mood&Content also contained the content and emotional
information, it performed even worse than the model with single-resource
information. This may be due to the huge gap between their semantic space
because they are designed to capture the different contextual presentations.
To address this discrepancy, Mood2Content guided the semantic space of the
content encoder close to that of the mood encoder through knowledge
distillation and learned to conduct depression detection simultaneously.

Table 5. Performance of early detection of depression with different models
and strategies.
( Mood&Content is a simple concatenation of mood and content representation.
Mood2Content is the proposed model which guides content representation with
mood representation.)

Model | AUPRC | AUROC
---|---|---
LIWC+LR | 0.2815 | 0.7017
TF-IDF+XGBoost | 0.4737 | 0.7933
HAN-500 tweets | 0.3219 | 0.7082
HAN-1000 tweets | 0.3269 | 0.7138
HAN-2000 tweets | 0.2623 | 0.6251
HAN-Daily | 0.6026 | 0.8621
HAN-Daily(BERT)* | 0.7359 | 0.9114
HAN-User* | 0.7649 | 0.9198
Mood&Content* | 0.5364 | 0.8447
Mood2Content* | 0.8116 | 0.9317

  * •

*means the best performance was reported

Table 6. Ablation study.
(HAN-Daily is the basic content encoder w/o user encoder and mood
representation; HAN-User includes the user encoder; Mood&Content concatenates
mood and content representation. Mood2Content is the proposed model.)

Model - Encoder | AUPRC | AUROC
---|---|---
HAN-Daily(BERT) - CTB | 0.6794 | 0.8939
HAN-Daily(BERT) - CTB-St | 0.5909 | 0.8422
HAN-Daily(BERT) - CTB-Emo | 0.4772 | 0.7421
HAN-Daily(BERT) - CTB-Tsa | 0.7359 | 0.9114
HAN-User - CTB | 0.7499 | 0.9194
HAN-User- CTB-St | 0.7396 | 0.9098
HAN-User- CTB-Emo | 0.7428 | 0.9108
HAN-User- CTB-Tsa | 0.7649 | 0.9198
Mood&Content - CTB-St | 0.5514 | 0.8298
Mood&Content - CTB-Emo | 0.5364 | 0.8477
Mood&Content - CTB-Tsa | 0.5129 | 0.8235
Mood2Content - CTB-St | 0.8029 | 0.9313
Mood2Content - CTB-Emo | 0.7978 | 0.9299
Mood2Content - CTB-Tsa | 0.8116 | 0.9317

##  5\. CASE STUDY

Our framework’s attention-based user encoder allows us to visualize the impact
of daily tweets on the final depression prediction. This is accomplished by
analyzing the assigned attention weight for each day. The daily attention
weight of a positive case is depicted in Table 7, with a darker background
color indicating a greater attention weight. On days 0, 2, and 16, the patient
posted more desperate tweets, which are characterized by greater attention
weights. This demonstrates that not only can our framework accurately predict
depression risk but also estimate risk days. It is possible to delve deeper
into the relationships between depression and social factors among a large
number of depression patients by incorporating additional information such as
weekdays vs. weekends or holidays vs. normal days. In addition, Table 7 also
lists the daily emotion of the patient, and we discovered that our model does
not always emphasize negative emotions (such as sadness and anger). This
suggests that the context-based model has a different emphasis than emotions,
and the combination of both information sources can result in more accurate
predictions.

Table 7. A case study of COVID-19 patient at depression risk.
(Each daily tweet was performed emotion recognition by CTB-Emo and colored by
its weight to user presentation.)

Days (Before) | Emotion | Daily Tweet
---|---|---
Day 28 | Sadness |  I have had 3 telemed visits with my doctor. They are not really seeing people in person in my area…
……
Day 17 | Optimism |  Ongoing mission to find new life and new civilizations. Boldly go where no one has gone before.
Day 16 | Optimism |  I have covid, I have the antibodies. I was only very sick for 4 days. Then my immune system kicked in and i felt much better. it does not have to be a long battle. stay up as much as possible. don’t take it laying down. don’t sleep laying flat. eat and drink plenty. This is how i want to die.
Day 15 | Optimism |  @user glad we talked about your problems and made it over that. each one teach one. …
……
Day 8 | Sadness |  I developed a mental health disorder in which I crave ice cream. New song upload. …
Day 7 | Anger |  @user Yes, I believe only the best can apply for police, the only problem is that only #offensive and #offensive want this job now.
……
Day 1 | Null* |  This is not bf6. It is a demo of dice current tech, probably taken from the last update of bf5. With less drug testing, less probation violations.
Day 0 | Anger |  … I’m sure investigation will uncover solid evidence of a liberal conspiracy. It made me a mental case for a month after i had it. …

  * •

*Null indicates that the probability of all emotions is less than 0.5.
$\\#$Offensive words have been replaced

##  6\. ETHNIC CONSIDERATION

For the protection of vulnerable individuals, privacy and ethical
considerations are of paramount importance in the field of mental health.
Using publicly accessible data collected via Twitter’s official API, our study
adheres to these stringent requirements. Our research utilized tweets obtained
in accordance with Twitter’s Privacy Policy, which informs users that the
content they post on the platform, including their social profiles and tweets,
is public and freely accessible to third parties. To protect individual
privacy, we omitted usernames from our study and only provided the Tweet ID
for download via the Tweet API.

##  7\. CONCLUSION

The COVID-19 pandemic has been three years, but the negative impact of
COVID-19 still exists and tends to last for a long time. One critical social
problem is the mental health risk of COVID-19 patients. COVID-19 triggers a
non-trivial increase in depression patients. To alleviate this problem, one
crucial step is to detect depressed COVID-19 patients as soon as possible and
conduct an early intervention. This paper targets this critical social problem
and the contributions of this paper are three folds: 1) We propose a novel
research topic: predict the early depression risk with social media data; 2)
We build a dataset from Twitter users which consists of 10,656 COVID-19
patients. 1,776 are positive cases who will emit depression signal after
infection; 8,880 are in the control group who don’t get depressed after
infection. For each positive user in this dataset, we have the timestamp of
COVID-19 infection and depression signal emergence as well as all posted
tweets; 3) We also propose the Mood2Content model which manages to detect
early depression risk. Mood2Content achieves an AUROC of 0.9317 in predicting
the depression risk two weeks ahead of time, which outperforms baseline models
ranging from popular machine learning models to pre-trained large language
models. This enables the feasibility of early intervention of depressed
patients.

##  8\. LIMITATION

Several potential limitations should be considered for this study. First,
although we have taken numerous steps to identify eligible individuals as
precisely as possible, it is possible that the dataset still contains some
false positive cases. However, manual validation was performed to confirm the
dataset’s dependability, and the vast quantity of social media data helps to
mitigate this issue. Second, we only encoded the first 256 tokens of daily
tweets as sentence embeddings in order to meet the length limit of large
language models and improve model efficiency. It may result in some
information loss. Nonetheless, the threshold of 256 tokens covers 93% of
tweets, which mitigates the issue to some extent. Lastly, we did not use
information about COVID-19 disease, such as symptoms, to enhance the model
performance. We intend to investigate this in future studies.

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