The COVID-19 pandemic broke out in China in December 2019, with the city of Wuhan as its epicenter. To contain the spread of the virus, the Chinese government put Wuhan into lockdown on January 23, 2020, by imposing restrictions on travel to and from the city [1]. More rigorous lockdown measures, including limits on outdoor activities and community containment, were gradually implemented in the following weeks [2]. Although lockdowns can effectively limit the spread of a virus [3], these measures seriously interfere with citizens’ daily routines and social interactions [4]. Studies have found that travel restrictions and social distancing can lead to negative emotions such as distress, anger, and fear [5,6]. In this study, we sought to comprehensively map the emotional trajectories of Chinese people during the Wuhan lockdown using Weibo posts as our data source.

Understanding people’s emotions during public health emergencies is important. At the individual level, emotional experiences are related to mental health. Negative emotions (eg, hostility and fear) during the pandemic were found to mediate the positive correlation between exposure to stressful events (eg, experiencing a lockdown and witnessing the death of loved ones) and symptoms of anxiety and depression [7]. Positive emotions, by contrast, were positively correlated with resilience, and the correlation was stronger among people who had experienced a higher level of negative emotions in the preceding week [8,9]. High-arousal emotions (eg, anger and fear) were found to be associated with poor sleep quality, stress, and anxiety during the pandemic [10,11]. Although emotions with low arousal (eg, calmness and boredom) make people feel relaxed, they have been found to be associated with depression [12] and a loss of meaning in life [13]. People’s emotions may also contribute to behavioral outcomes in the context of public emergencies. For example, there is evidence that pleasant (eg, happiness) and unpleasant (eg, fear and anxiety) emotions were related to compliance with social distancing measures during the pandemic [14-17]. Heffner et al [18] found that pleasant emotions with higher arousal elicited by prosocial messages about COVID-19 were correlated with greater compliance with social distancing. The unrelenting and repetitive living situation was found to be associated with emotions with low arousal (eg, boredom) during the pandemic and stimulus-seeking behaviors such as impulsive buying after the pandemic [19]. Although it is clear that lockdown measures affected people’s emotions, research findings are far from conclusive.

Although many countries implemented lockdown measures during the pandemic [20], people’s emotional responses following the Wuhan lockdown were unique for several reasons. First, the Wuhan lockdown occurred at the beginning of the pandemic. Owing to the public’s lack of knowledge and preparation for the outbreak of a novel disease, the abrupt lockdown triggered particularly strong emotional reactions, including fear, anxiety, and hopelessness [21]. Second, the lockdown measures in China were more strictly implemented than anywhere else in the world because a wide range of local institutions (eg, residents’ committees and property management companies) were mobilized [22]. Third, the Wuhan lockdown was implemented just before the Chinese New Year, a major holiday in Chinese communities. The mobility restrictions prevented individuals from participating in significant activities such as family reunions, leading to intense emotional responses (eg, anxiety, disappointment, anger, and hostility) and worsened psychological well-being [2,4,23,24].

Studying the emotional responses of Wuhan residents can be important as those who were physically restricted by the lockdown measures were more emotionally vulnerable to pandemic-related events. Studies have found that residents of Wuhan and the rest of the Hubei province were sensitive to milestone events (eg, the lockdown announcement on January 23, 2020, and the death of Dr Li Wenliang on February 7, 2020) and expressed more negative emotions after the lockdown was imposed than people from elsewhere in China [2,4,25,26]. In this study, we used sentiment analysis of Weibo data and the circumplex model of affect to examine the emotional responses of Chinese people in general and those living in Wuhan in particular.

During the pandemic, social media became a major source of information about the public’s emotions in response to lockdowns [27]. Unlike surveys, which rely on retrospective self-reports, social media data include real-time spontaneous feelings from a wide variety of people and are free from recall bias and demand characteristics [25,28,29]. Starting from the outbreak of COVID-19, a massive wave of discussions took place on Sina Weibo, one of China’s most popular social media platforms [2,27]. In March 2022, Weibo had 582 million active users per month and 252 million active users per day [30]. Emotion information embedded in posts on Weibo provides a unique source of data for researchers to investigate emotions in response to the Wuhan lockdown. Therefore, in this study, we used Weibo posts to map the emotional trajectories of Chinese people before and after the lockdown was imposed.

Using social media data, recent studies have found an increase in negative emotions such as fear, panic, guilt, anger, and disappointment [2,4,25,26,28,31] as well as an increase in positive emotions such as feeling blessedness, hope, admiration, and encouragement after the Wuhan lockdown [2,26,32]. It appears that Chinese people experienced increases in both pleasant and unpleasant emotions after the lockdown was imposed. However, researchers have used several discrete emotion categories to code social media data. For example, Su et al [26] used the negative emotion and affect categories from the Simplified Chinese Linguistic Inquiry and Word Count lexicon [31]. Su et al [28] and Yu et al [25] adopted “depression,” “like,” “dislike,” “anger,” “sadness,” “fear,” “enjoyment,” “disgust,” and “surprise” from the Affective Lexicon Ontology [33]. Shen et al [2] manually coded emotion words from corpora as “anger,” “fear,” “neutral,” “encouragement,” and “hope.” Cao et al [32] used the Ortony, Clore, and Collins model and the 6 emotions by Ekman to categorize emotions as “joy,” “hope,” “distress,” “fear,” “admiration,” “reproach,” and “neutral.” The limited emotion types used in previous studies might not be sufficient to construct a complete picture of emotional responses during the Wuhan lockdown. Applying a sentiment analysis based on the circumplex model of affect, this study sought to map the comprehensive emotion trajectories of Chinese people during the Wuhan lockdown.

The circumplex model of affect defines emotion in a Cartesian space with 2 independent dimensions and has received support in various cultural settings [34-37]. The horizontal axis, valence, denotes the degree of pleasantness and unpleasantness, and the vertical axis, arousal, denotes the degree of activation and deactivation. An emotion is composed of different levels of valence and arousal. For instance, although both calmness and encouragement are pleasant emotions, the former is less activated than the latter. Compared with categorical models in which emotions are restricted to a certain number of categories, the circumplex model allows for a potentially infinite number of emotions, thus enabling a more fine-grained analysis of emotion in sentiment analysis [38].

Although previous research findings have shown that both pleasant and unpleasant emotions were related to compliance with social distancing during the pandemic, the arousal dimension of the circumplex model of affect has been largely overlooked. For example, Shen et al [2] sorted emotions into “positive,” “negative,” and “neutral” categories, neglecting the arousal dimension. Researchers have mainly focused on high-arousal unpleasant emotions such as fear, panic, and anger [28] while neglecting low-arousal emotions such as calmness and glumness [36]. To obtain a full picture of the emotional trajectories of Chinese people in response to the Wuhan lockdown, we used a lexicon based on the circumplex model of affect for sentiment analysis in this study.

Understanding citizens’ emotional responses to extreme social distancing measures such as a lockdown provides insights into how governments can effectively respond to a pandemic. The Wuhan lockdown certainly served as a catalyst for research on mapping the emotions of those affected by lockdown measures, but the findings remain far from conclusive. The aim of this study was to examine the emotional trajectories expressed on Weibo using a sentiment analysis approach. Specifically, we sought to answer three research questions (RQs): (1) What were the emotional trajectories of Chinese people during the Wuhan lockdown? (RQ 1); (2) Were there any differences in immediate emotional responses to the lockdown between people from Wuhan and those from non-Hubei areas? (RQ 2); and (3) Were there any differences in the longer-term impact of the lockdown on emotional responses between people from Wuhan and those from non-Hubei areas? (RQ 3).

To protect human participants’ privacy, all users’ personal information was anonymized during our analysis. This study focused on human emotions at the aggregated level, meaning that individual-level information was averaged across users and no individual information was disclosed.

We cleaned the posts before assigning emotion scores. Figure 1 summarizes the process of data cleaning and sentiment analysis. Weibo posts have two major formats: original posts and reposts. An original post contains the original comments made by a user. A repost usually has three parts: the original message being reposted, other users’ comments about the original post, and the reposting user’s comments about the original post. As previous studies found that including the message being reposted in sentiment analysis significantly reduced the unpleasantness of emotions in Weibo posts, we excluded the original message being reposted and previous users’ comments and analyzed the comments by the reposting user only [42]. For example, a repost might read, “Hey guys, these are the new disinfection guidelines by our hospital, please repost to let more people know. Reason for repost: Very good. Thank you very much! //@PriorUser1: Sounds good, thank you.” In this repost, only the phrase “Very good. Thank you very much!” is an original comment made by the reposting user and would be subjected to our sentiment analysis. However, if the reposting user added no comments, we conducted sentiment analysis on all previous users’ comments to represent the reposting user’s comment as previous studies have found that Weibo users only repost other users’ comments when they agree with them [43,44].

During data cleaning, we first retained the original posts and the comments made by the reposting user. We then removed tags, usernames, and web links from each post as such content would add noise to the analysis. The remaining content of each post was retained for sentiment analysis.

A lexicon-based sentiment analysis was conducted to assign each post a valence score and an arousal score based on the Chinese Sentiment Lexicon for Internet (CSLI) [45], which consists of 7088 Chinese words, each of which is annotated with a valence score ranging from −4 to 4 and an arousal score ranging from 0 to 8.

Our sentiment analysis procedure is summarized in Figure 1. The JiebaR package in R [46] was used for tokenization, which involved extracting the emotion words listed in the CSLI from each post. Following the procedures by Zhao et al [45], we calculated the valence and arousal scores of each post using the weighted average value. For each post, the valence score of each emotion word was multiplied by its frequency—the products of all emotion words in a post were summed and then divided by the total frequency of all emotion words in the post. The same method was applied to calculate the arousal score. Mean valence and arousal scores were assigned to each post to indicate its emotion. Using Weibo posts, Zhao et al [45] validated this sentiment analysis method by comparing valence and arousal scores rated by participants with the predicted CSLI scores for the same posts. The convergence was 0.70 for valence and 0.59 for arousal. As valence in the CSLI lexicon has a range of −4 to 4, we defined posts with valence scores <0 as unpleasant and scores >0 as pleasant. Similarly, as arousal has a range of 0 to 8, we defined posts with arousal scores <4 as low-arousal and scores >4 as high-arousal.

For subsequent analyses, we computed 2 types of daily emotion scores using the valence and arousal scores of posts yielded by the CSLI lexicon. One was daily emotion by person. For example, a daily valence score by person was calculated for a user by averaging the valence scores of all posts made by the user on that day. A daily arousal score by person was calculated in the same way. The other type of daily emotion score was emotion by day. A valence score by day was calculated by averaging all daily valence scores by person across users who created posts on a particular day. An arousal score by day was calculated in the same way.

People who were physically restricted by the lockdown measures might have had different emotional responses from people who were not in the lockdown area. A previous study found that Hubei residents expressed more negative emotions than those living elsewhere in China during the pandemic [2]. To examine the differences in emotions in response to the Wuhan lockdown between Wuhan and non-Hubei areas, we grouped the Weibo users in data set 2 into a Wuhan group (ie, the lockdown area) and a non-Hubei group (ie, nonlockdown area) based on their postlockdown geographical location. The non-Hubei area, rather than the non-Wuhan area, was used as Wuhan was not the only city in the Hubei province that was locked down after January 23, 2020. A total of 15 other cities in Hubei gradually implemented similar restrictions up to January 27, 2020 [47]. Therefore, to compare the pre– and post–lockdown announcement periods, we decided to focus on the non-Hubei rather than the non-Wuhan area.

To differentiate between Wuhan and non-Hubei users, the IDs of users who made posts after the announcement that contained geotags (ie, the GPS coordinates of a user’s device, including longitude and latitude) were filtered for geographical information [48]. As people could no longer enter or exit Wuhan after the lockdown was imposed, the users’ GPS information enabled us to assess their actual geographical locations after the lockdown was imposed.

We used data set 2 to explore the emotional responses of the Wuhan and non-Hubei groups. We first used the geotags to separate user IDs into Wuhan and non-Hubei groups. All posts with these user IDs were then extracted, resulting in 6665 posts generated by 1236 users in Wuhan and 51,020 posts generated by 12,714 users outside Hubei. These users all made at least one post before and one post after the lockdown was imposed. It is important to note that Weibo users can select geotags from a list of locations, allowing them to misreport their actual locations. This function is an unfortunate drawback of Weibo data and should be treated as a source of error [48].

We used data set 2 to examine how people’s everyday emotions evolved from 2 weeks before to 2 weeks after the lockdown was imposed. As data set 2 had a nested data structure in which repeated measures of emotion ratings were nested within each user, we used multilevel modeling [49]. A multilevel model separates the residual variances of a sample into within-person (level 1) and between-person (level 2) variances [50]. Multilevel modeling allows for data dependency [51,52], meaning that the measures of one person would be more similar to one another than to the measures of another person. In addition, social media data are not always complete, meaning that there would be missing observations if an individual did not post anything on certain days. Multilevel modeling is suitable for social media data as it can tolerate missing observations [51].

Piecewise multilevel models can capture changes in groups during different phases [53] and can therefore be used to compare the changes in emotional trajectories between the Wuhan and non-Hubei groups before and after the lockdown was imposed. The number of posts in data set 2 was small before January 20, 2020, as widespread discussion of pandemic-related topics had not yet started [27,41]. To ensure adequate observations of emotion ratings across users per day (ie, >50 users per day) [54], we used data set 2 data starting from January 20, 2020, for the multilevel modeling. The R package lme4 [55] was used to estimate the multilevel models.

The data in data set 1 included 359,190 posts made by 242,023 users. To map the emotion distribution of Weibo posts, the valence and arousal scores of each post were used to draw the scatterplot shown in Figure 2. The valence and arousal combinations are depicted by their frequency. Posts were classified as unpleasant when their valence scores were <0 and as pleasant when their valence scores were >0. Posts were classified as low-arousal when their arousal scores were <4 and as high-arousal when their arousal scores were >4.

Overall, the emotions in the posts spread across the valence-arousal plane, covering both the pleasant and unpleasant areas. Highly frequent combinations of valence and arousal scores were gathered in low-arousal areas and leaned toward the pleasant half of the plane. Several highly frequent valence-arousal combinations were in the pleasant and high-arousal quadrants (valence >0; arousal >4).

Of the 359,190 posts, 248,757 (69.25%) fell in the pleasant and low-arousal quadrant (valence >0; arousal <4), 72,773 (20.26%) fell in the unpleasant and low-arousal quadrant (valence <0; arousal <4), 32,850 (9.15%) fell in the pleasant and high-arousal quadrant (valence >0; arousal >4), and 2023 (0.56%) fell in the unpleasant and high-arousal quadrant (valence <0; arousal >4). The remaining 0.78% (2787/359,190) of posts were neutral in valence or had a moderate arousal of 4. These results showed that, among all the combinations of valence and arousal, most posts (282,068/359,190, 78.53%) made from January 9, 2020, to February 6, 2020, were pleasant, among which there were more low- than high-arousal emotions.

To evaluate the relationship between the valence and arousal of posts on Weibo, a quadratic regression was fitted to the 359,190 posts. The results showed a significant quadratic relationship between valence and arousal (F_2,359,187=1.554×10⁵; P<.001; R²=0.464). The regression equation was arousal = 2.156 + 0.046 × valence + 0.368 × valence². As shown in Figure 2, both pleasant and unpleasant emotions were accompanied by activation (refer to the study by Yik et al [37]).

To explore the emotional trajectories before and after the lockdown was imposed (RQ 1), the data in data set 1 were analyzed. We averaged the valence of all posts made on each day and plotted the daily valence scores over the 4 weeks. Figure 3A displays the daily trajectories in valence (−4 to +4). On January 20, 2020, when human-to-human transmission of COVID-19 was confirmed [25], a turning point emerged during which the valence score increased until January 23, 2020, and then reached a plateau and stabilized at a higher level. On February 3, 2020, a day after all patients infected or suspected to be infected with SARS-CoV-2 were required to be quarantined and isolated [25], valence rose to a higher level than the week after the lockdown announcement, reaching a second plateau. The results show that the emotions expressed on Weibo became more positive starting on January 20, 2020, 3 days before the lockdown. After the lockdown was imposed, a higher level of pleasant emotions was expressed, although valence was generally pleasant (valence >0) at a low level (arousal <4). Two valence plateaus were observed: one from January 23, 2020, to February 2, 2020, and one from February 3 to 6, 2020.

Similarly, we averaged the arousal of all posts made on each day and plotted the daily arousal scores over the 4 weeks. Figure 3B displays the daily trajectory of arousal scores (0 to 8). The plot shows that the turning point of arousal occurred on January 19, 2020, after which arousal increased until January 23, 2020, and then reached a first plateau and became stable. From February 3, 2020, arousal rose to a higher level than the week after the lockdown was imposed and reached a second plateau. These results show that the emotions expressed by Weibo users became more activated during the lockdown, although the daily arousal level was generally low. Two arousal plateaus were observed: one from January 23, 2020, to February 2, 2020, and one from February 3 to 6, 2020.

To investigate the reasons for the 2 valence and arousal plateaus during the lockdown, we conducted separate lexical analyses on the word frequencies across all words that were used during the 2 plateau periods. Word clouds, in which more frequently used words appear in larger font sizes, were used to visualize word frequency and demonstrate the main themes of the posts [56]. The 100 most frequently used words in the 2 weeks before the lockdown (January 9, 2020, to January 22, 2020) are illustrated in Figure 4A. The word 兄弟 (brothers) was the most commonly used word in the 2 weeks before the lockdown as fans of a music band named 摩登兄弟 (Modern Brothers) were doing promotion activities. This result provides a picture of Weibo topics before the lockdown, during which people on Weibo engaged in celebrity promotion activities and discussed topics other than the COVID-19 pandemic [57,58].

As shown in Figure 4B, of the 100 most frequently used words in the 2 weeks after the lockdown announcement (January 23, 2020, to February 6, 2020), 加油 (jiayou or add oil, which is a term of encouragement) had the highest frequency, accounting for 4.32% (81,969/1,895,476) of all emotion words found in the posts. The prevalence of jiayou increased to 6.48% (57,239/883,447) from February 3 to 6, 2020. Notably, jiayou accounted for only 0.53% (6734/1,277,449) of emotion words in the 2 weeks before the lockdown. In the CSLI lexicon, jiayou has a valence score of 2.000 (ranging from −4 to +4) and an arousal score of 5.143 (ranging from 0 to 8). It is likely that the escalating valence of Weibo posts during the lockdown can be attributed to an increased use of words of encouragement such as jiayou.

Data set 2 included 6665 posts made by 1236 Wuhan residents and 51,020 posts made by 12,714 non-Hubei residents from 2 weeks before to 2 weeks after the lockdown announcement. Each user made at least one post before and one post after the announcement. To examine the daily valence trajectories of Wuhan and non-Hubei residents, we averaged the daily valence across users on each day to generate daily valence scores for the Wuhan and non-Hubei groups. The daily arousal trajectories of Wuhan and non-Hubei residents were generated using the same method. Figure 5A shows the daily valence trajectories of the Wuhan group and the non-Hubei group during the same period. Before human-to-human transmission of the virus was confirmed on January 20, 2020, the valence of the Wuhan group fluctuated more dramatically than that of the non-Hubei group, possibly because the data points were dominated by a small amount of data across individuals per day. Before January 20, 2020, the number of people who created posts per day in the Wuhan group ranged from 1 to 45, whereas the number of people who created posts per day in the non-Hubei group ranged from 11 to 91.

From January 20, 2020, the number of people who made posts per day dramatically increased in both the Wuhan and non-Hubei groups to a minimum of 162 individuals per day, which is consistent with the findings of Lu et al [27] and Hu et al [41], who reported a dramatic increase in Weibo-COV2 data after January 20, 2020. From January 20, 2020, the daily valence of the Wuhan and non-Hubei groups became parallel and increased steadily until the lockdown announcement on January 23, 2020, when the valence of the non-Hubei group peaked but that of the Wuhan group remained at a similar level to that of the previous day. After January 23, 2020, the daily valence of both groups increased and became similar across days. Notably, unlike the non-Hubei group, the Wuhan group did not express more pleasant emotions on the day of lockdown.

Figure 5B shows a plot of daily arousal trajectories of the Wuhan and non-Hubei groups during the same period. The trajectories of both groups became parallel and increased after January 20, 2020, before diverging on January 23, 2020, when arousal peaked in the non-Hubei group but remained steady in the Wuhan group. After the lockdown was imposed on January 23, 2020, the daily arousal trajectories of both groups stabilized at a higher level and became parallel across days. Unlike the non-Hubei group, the Wuhan group did not express more activated emotions on the day of lockdown.

Using the Wuhan lockdown as our context, we sought to understand the interplay between lockdown measures and the emotions of residents in different areas of China. Our results showed that, during the lockdown, most posts on Weibo were pleasant, with low arousal. Compared with posts before the lockdown, posts after the lockdown was imposed had higher valence and arousal, indicating more pleasant and activated emotions. Word cloud analysis revealed an increased use of the encouragement word jiayou after the lockdown was imposed, which might account for the increase in valence and arousal. Non-Hubei users’ valence and arousal were significantly higher on the day of lockdown than on the day before and after the lockdown was imposed. In contrast, Wuhan residents showed little immediate change in emotion in response to and after the Wuhan lockdown was imposed. Overall, in the 2 weeks after the lockdown was imposed, the valence and arousal scores of Weibo users remained constant regardless of their geographical location.

In contrast to studies that observed more negative emotions during the lockdown, our overall emotion trajectories suggest that Weibo users demonstrated more pleasant and activated emotions after the lockdown was imposed. A possible explanation for the discrepancies between our results and those of previous studies is the differences in word choice when coding the data. Studies that reported more negative emotions after the lockdown was imposed tended to use emotion types such as “stress,” “hostility,” “disappointment,” “surprise,” “fear,” “guilt,” and “blame” [2,4,25,28,32], and those that reported positive emotions after the lockdown was imposed tended to include emotion types such as “encouragement,” “admiration,” “hope,” and “blessedness” [2,28,32]. The word choices could have biased their results toward conclusions that were determined by the word categories chosen. Instead of focusing on a few selected emotion categories, we chose a lexicon based on the circumplex model of affect in which its two dimensions (valence and arousal) underlie most—if not all—emotions [34-36]. In this model, emotions are composed of valence and arousal, meaning that both pleasant and unpleasant emotions with different levels of arousal are covered.

Unlike non-Hubei people, who were significantly affected, people in Wuhan experienced smaller immediate changes in emotions in response to the lockdown event. Unlike the temporary peak in valence and arousal found in non-Hubei areas on the day of lockdown, there was a minimal change in valence and arousal for Wuhan people. This result appears to support the psychological typhoon eye effect, which describes the phenomenon of people closer to the epicenter of a devastating event having less intense psychological reactions to it [61]. Zhang et al [62] concluded that people closer to the outbreak displayed fewer mental health problems in response to the COVID-19 pandemic as they had a more accurate estimation of the situation and were desensitized by repeated exposure to stress.

Although the emotions of Wuhan people were more stable than those of non-Hubei residents on the day of lockdown, their emotional changes were similar to those of non-Hubei people over the longer term. Overall, Chinese Weibo users, regardless of whether they were in lockdown or not, expressed stable emotions with higher valence and arousal in the 2 weeks after the lockdown was imposed than before the lockdown. This result is in stark contrast to that of the study by Zhao et al [4], who found fewer positive emotion words (eg, “happiness”) and more negative emotion words (eg, “fear” and “pain”) in Wuhan than in non-Hubei areas after the lockdown was imposed. The difference may be rooted in the fact that, unlike our study, which selected data from up to 2 weeks after the lockdown was imposed (ie, January 23, 2020, to February 6, 2020), their data covered a longer postlockdown period (January 23, 2020, to February 16, 2020) that included the significant event of the death of Dr Li Wenliang on February 7, 2020. Li’s death was accompanied by dramatic increases in the number of posts and expression of negative emotions such as “anger,” “fear,” “surprise,” and “depression” [4,25,27], and the inclusion of this event may have negatively biased the emotional trajectories following the lockdown announcement.

We did not observe a systematic difference in valence and arousal scores between the Wuhan and non-Hubei groups after controlling for the moderating effect of location for slopes 2 and 3. That is, the average score of valence and arousal by person by day did not differ between people in or out of lockdown. This finding is different from a recent study by Meock et al [63] that compared the emotional responses to the Australian lockdown of people who were in and out of lockdown. The participants (both in and not in lockdown) in that study recorded their emotions 7 to 9 times per day for 1 week. Those in lockdown had slightly more negative emotions and slightly fewer positive emotions than those not in lockdown. A possible explanation for the discrepancy in results is the timing of data collection as Meock et al [63] collected data at a later stage of the Australian lockdown. Our study focused on the first 2 weeks of the Wuhan lockdown, during which the emotional consequences might not yet have emerged.

It is reasonable to expect negative emotions during a public health emergency such as the COVID-19 pandemic, but the power of positive emotions should not be overlooked. Our results highlight collective positive emotional responses during a public health emergency. Although Weibo users made both pleasant and unpleasant posts, their average daily emotions were pleasant and of low arousal throughout the 4 weeks under scrutiny. Unpleasant posts only accounted for 20.82% (74,796/359,190) of all posts during the lockdown. These findings are similar to those of Pan et al [64], who found that >50% of comments on Weibo during the pandemic were positive, and Arora et al [29], who found that happiness was the most prevalent emotion expressed by Indian Twitter users during the lockdown, accounting for 40% of users’ posts. Maintaining a positive emotional state may contribute to better mental health and lead to better coping when facing stressful events [62,65]. Positive feelings such as “calmness,” “hope,” “love,” and “gratitude” during the COVID-19 pandemic have been found to be positively correlated with a resilience mindset, and the relationship is stronger among people with a higher level of negative emotions (eg, “anger,” “anxiety,” “regret,” and “sadness”) than among people with low levels of negative emotions [9].

Why did Weibo users in China respond positively to the adverse situation after the lockdown was imposed? The cognitive processes of people in Eastern cultures are driven by Eastern naïve dialecticism, suggesting a tendency to see good in the bad and bad in the good [66,67]. Therefore, from a cross-cultural perspective, Chinese people are more likely than their Western counterparts to embrace adversity during a pandemic. Studies have found cultural differences in emotional responses during the pandemic. Yap et al [68] collected data on affect, optimism, well-being, and meaning in life from China and Canada in March 2020 and April 2020. Compared with the Canadian participants, the Chinese participants reported more positive and fewer negative emotions, greater optimism, greater psychological well-being, and greater sense of meaning during the pandemic. Our findings of higher valence and arousal after the lockdown was imposed, along with an abundance of encouragement words such as jiayou, add new evidence that Chinese people reacted more optimistically to the outbreak of COVID-19. Future studies could collect social media data from different countries to compare how people with different cultural backgrounds react to public health emergencies.

People tend to bond with strangers after an exogenous crisis, creating a sense of belonging to emergent groups [69,70]. Such collective cohesion and shared identity enable mutual support in adverse situations such as pandemics [71]. Research findings have also shown that a collective experience of positive emotions contributed to more resilience and better mental health during the COVID-19 pandemic [8]. Similar collective behavior was found during the Wuhan lockdown. In in-depth interviews by Qian and Hanser [21], Wuhan residents reported that they soon adapted to reality upon the abrupt announcement of the lockdown, with mutual support among neighbors on WeChat groups and in communities. Our results add further evidence for such collective behavior, with people on Weibo shouting jiayou together to support each other through the unfolding uncertainties of the pandemic. This finding is in line with the suggestion of Elcheroth and Drury [71] that the continuity of social ties might help people manage negative feelings during social crises such as pandemics and lockdowns.

Although the emotion trajectories of the Wuhan lockdown have been examined in previous studies, they have focused on limited types of emotions. Instead of coding social media data using limited types of emotions, we explored emotional responses to lockdown using a lexicon based on the valence and arousal dimensions of the circumplex model of affect [34-36]. Hence, our coding was not limited to a certain number of emotions. Rather, we sought to cover most if not all emotions.

The current study offers significant practical implications. In addition to providing an emotion map describing the valence and arousal of Weibo users, we offer explanations for the changes in emotional response patterns. We highlight the power of web-based social cohesion, which may explain the stability of Wuhan residents’ emotional states when the lockdown was imposed and the 2 weeks afterward. Such findings provide insights for policy makers to consider the reasonable length of lockdown measures.

However, our study is not without limitations. The first is the potential influence of censorship on our data sets. Posts that violate Weibo regulations are usually removed within 24 hours of their creation, meaning that real-time data scraping is crucial to understand the complete emotional picture on the web [27]. The observation that censored posts from the day of lockdown had a higher degree of criticism than of support whereas uncensored posts had similar amounts of criticism and support [27] might suggest that the real valence after the lockdown was imposed was lower than that detected in our study. To overcome this issue, future studies could consider using Weibo data collected in real time or within 24 hours after an event. Furthermore, as some posts contained words that were not coded in the CSLI lexicon, 7.38% (28,601/387,791) of the posts in data set 1 were excluded from the sentiment analysis. These posts may have contained emotion information. In the future, researchers could use machine learning and deep learning algorithms to capture the emotions embedded in the words that were not coded in the lexicon and map out emotional trajectories [2,32].

In this study, we applied a circumplex model of affect on 2 data sets of Weibo posts to explore emotional responses before and after the Wuhan lockdown was imposed. Our results suggest that, even while facing the adverse situation of a lockdown, the overall aggregated emotion expressed on Weibo was pleasant and of low arousal. Over time, the web-based discussion included more encouraging messages. In addition, compared with people from more distant areas, Wuhan residents had more stable emotions on the day of lockdown, but they showed similar changes to non-Hubei residents in the 2 weeks after the lockdown was imposed. Interestingly, the lockdown did not seem to subdue the feelings of Wuhan residents despite the fact that they were physically constrained. The increased valence and arousal of posts after the lockdown was imposed might suggest a collective cohesion that provides mutual support for people under adverse situations. Policy makers could consider various approaches to forming and maintaining social connections for mutual encouragement and better adjustment during lockdowns and public health emergencies.