Topic and Emotion Development among D utch COVID -19 T witter Communities in the early Pandemic (2020)

Abstract: The paper focuses on a large collection of Dutch tweets from the Netherlands to get an insight into the perception and reactions of users during the early months of the COVID-19 pandemic. We focused on five major user communities of users: government and health organizations, news media, politicians, the general public and conspiracy theory supporters, investigating differences among them in topic dominance and the expressions of emotions. Through topic modeling we monitor the evolution of the conversation about COVID-19 among these communities. Our results indicate that the national focus on COVID-19 shifted from the virus itself to its impact on the economy between February and April. Surprisingly, the overall emotional public response appears to be substantially positive and expressing trust, although differences can be observed in specific group of users.

## introduction during the early covid-19 pandemic, twitter has played a key role in facilitating communication from government agencies and officials, but also among members of the general public. twitter content has thus had a major influence on the public perception and sentiment surrounding the developing covid-19 situation around the world. in this work, we conduct an exploratory study to investigate potential quantitative differences in topic dominance and emotional content between different user communities. to better focus our study, we restrict the the analysis to dutch twitter during the first three months of the covid-19 pandemic. many individuals rely on twitter for information. in the united states, 68% of people reported social media as their "news-outlets" @xcite , with a third of people also claiming that social media is an important source of health and science information @xcite . these figures are not surprising, since many official agencies, e.g. the government and health organizations, increasingly use twitter to communicate key information to the public. this is in part because research has shown that twitter can be particularly effective during rapidly developing events, such as disasters, political unrest, and outbreaks @xcite @xcite . for example, during the 2015 zika virus outbreak, credible organizations such as the world health organization (who) and the center for disease control (cdc) used twitter to circulate important health information @xcite . as there is no gate-keeping on the platform, content quality varies widely, from informal comments by private individuals to official communications from the government. some content is made up of misinformation, rumors, and false statements, that are then massively circulated via the platform @xcite @xcite . for this reason, studying the topics and emotional content of different user communities around a major public event (the covid-19 pandemic) can give us important insights into both the public perception of the situation, the response of official agencies, and the general sentiment as the pandemic developed. the first case in the netherlands was reported on the 27th of february. 1 while a growing number of papers that analyze twitter activity during the pandemic have already come out @xcite @xcite , they largely focus on the situation in the united states, or other english speaking countries. instead, we focus primarily on tweets from the netherlands or belgium, only considering dutch language data. this allows us to study the direct relation between disease spread, government response and the public sentiment on a more local scale. dutch tweets are almost exclusively tweeted by individuals or organizations within the netherlands, the flemish area of belgium, or, to a lesser extent by dutch individuals abroad.this contrasts with english tweets which are more heterogeneous since english has a larger group of speakers and users. in particular we address the following questions: rq1 how varied is the discourse around covid-19 by dutch speaking communities? what topics dominate? rq2 do different users (e.g., politicians, journalists, influencers, active and casual users, among others) focus on different topics related to the covid-19 outbreak? rq3 do different users express different emotions about the covid-19 outbreak? the main contributions of this paper can be summarized as follows: (1) we conduct an extensive analysis of a newly created and publicly available dutch twitter dataset @xcite covering the first three months of the covid-19 pandemic; (2) we found commonalities across user communities, but also different identifiable focuses, and (3) we identify community differences also in the emotional spectrum. our analysis characterizes the evolution of the discourse around covid-19 in dutch speaking communities between february and april 2020, both in the topics that dominated and in the emotions expressed that reflect differences across twitter communities during this virtual national conversation. ## related works twitter has been largely used as a proxy of natural language data for the study of different sociodemographics issues of a target population. previous work ranges from the identification of personality traits @xcite , expressions of emotions @xcite , language use @xcite , age @xcite , gender @xcite , and authorship attributions @xcite , among others. previous attempts at community detection of twitter users have used numerous methods and classification types, categorizing users based on their tweet context @xcite , interaction and topics in the network @xcite , sentiment oriented approaches @xcite and followingto-followers ratio @xcite . topic modeling has been widely and successfully used as a distant reading method to explore massive amounts of data and identify clusters of information @xcite @xcite . the application of these approaches to twitter data is a challenging task due to the short nature of the texts @xcite . previous work has mainly applied latent dirichlet allocation (lda) based methods @xcite with varying degree of success. the main issue is that lda works under the assumption that a document can belong to multiple topics, where the membership is determined by some probability over the words. since tweets are short snippets of text communicating often one single thought, they are instead more likely to contain only a single topic (2018). for this reason, we make use of the recently proposed semantics-assisted non-negative matrix factorization method (seanmf), @xcite . there is already a growing number of published research studies where a range of different analyses, including sentiment and public response, have been applied to covid-19 social media @xcite . however, as far as we know, no research has focused in detail on a single country and its social media response. ## data collection and cleaning we conducted our analysis on a newly created dataset, 40twene nl (caselli and basile, 2020). 2 the messages have been extracted from an on-going and continuous collection of twitter messages in dutch @xcite by means of a selection of relevant keywords. keyword identification was done manually by monitoring a trend website foot_3 from february to april 2020. for each day, top trending and covid-19 related hashtags were extracted. to ensure variability and limit biases, the selection of the keywords was conducted by considering the trends during different moments of each day (i.e., morning, afternoon, evening, night). this resulted in 39 unique keywords which have been applied to obtain the messages in the selected periods of time. the keywords are not preprocessed (e.g., lemmatization or stemming) and instead only exact matches were looked for in hashtags or messages. retweets have been excluded since they would have added noise. the dataset contains 2,390,596 tweets and only the twitter ids are distributed. we have used hydrator foot_4 to retrieve the texts of the tweets and all associated metadata, including the geolocalization, the user screen name, the user name, the user description, the timestamp, the number of likes, the number of retweets, the geographical coordinates, and the place. the use of keywords may have introduced bias in the selection of the data by excluding non-dutch speaking users living in the netherlands or belgium. this may result in a less representative analysis of the whole populations, with some minority voices "not being heard". ## emotional and discourse analysis of users' language to gain better insightswe computed two emotional prior scores for the messages by means of a lexicon look up approach. the lexicon used for this was the dutch version of the nrc emotional lexicon (mohammad and turney, 2013) that contains emotion association scores for 14,182 words for 10 different emotions, each represented by a score between 0 or 1. two additional columns for the word lemma and stem have also been added, to increase the likelihood of finding a match. the first emotional score, called polarity score, assigns a positive or negative score to every message on the basis of the scores associated to the entries in the nrc lexicon. to make the score operational, emotions that in the lexicon that are deemed to be negative (e.g., "anger", "disgust", "fear" and "sadness"), have their scores inverted and ambiguous ones have been excluded. to compute this score, we go over each word in every tweet and attempt to find a match in the lexicon. if an entry is found, all associated scores for the emotional categories are added up. in case multiple entries are found, the scores are again added up and an average score is returned. this score indicates the actual emotion directionality carried by a tweet, influenced by positive and negative words within it, rather than the dominant emotion label. the second emotional score, that we called emotional load, aims at assessing the emotional weight of the messages, i.e., how many emotions a message has. we compute the users' emotional load by assigning a point any time that a word in a message matches an entry in the nrc lexicon. the procedure of computing the score is similar to that of the polarity score, however the lexicon is left unchanged and negative emotions are not inverted. for each found word in a tweet, the scores in the entry are added up. similarly as before, in case multiple entries are found, the total added scores are averaged out. emojis are also included in the score calculation of both measures. a different lexicon was used for this @xcite , with the lexicon containing sentiment scores for each emoji. for the emotional load, each detected emoji simply adds 1 to the score, while for the polarity score, the actual emotion score of the corresponding emoji is used. we applied these measures to specific groups of users corresponding to socio-demographic categories of interest. in particular, we wanted to investigate if there are differences among five major societal actors: governmental and public health organizations, news media (i.e., tvs, radios, newspapers), politicians, the general public, and promoters/supporters of conspiracy theories. these groups have been selected as they appear to play different roles with respect to the impact of covid-19. governmental and public health organizations are in charge of everyday management and decisions which impact on the population; news media are responsible for setting the tone and the narratives about covid-19; politicians represent the connecting elements between the government and the different groups of interest they represent; the general population is somehow a passive actor who undergoes the decisions of the government; conspiracy theory activists promote "alternative facts" on covid-19, are responsible for spreading misinformation, and build narratives that directly oppose those of governments and news media. the identification and clustering of the users in socio-demographic categories has been conducted as follows: first, we have distinguished the authors of the tweets into verified and non-verified users. user names are cleaned to only include alphabetic characters. we then conducted a semi-automatic automatic entity linking by associating the screen names to wikipedia entry. ideally the screen name would return a single entry with the needed information in the form of a summary page. each retrieved wikipedia summary is then further matched with a set of pre-defined keywords characterizing every target group extracted from the user description. in case a screen name returned multiple wikipedia entries, a summary is taken from each and the matching process repeated. after this first pass, we manually checked that the automatically assigned labels were correct. a limitation of this strategy is that the lack of wikipedia entry places the target users in the general population. as a strategy to compensate this potential generalization of users in the general public demographic, we further distinguished in the general population all verified accounts from the rest. verified accounts in twitter are used to signal the authenticity of the accounts of users of public interest. the conspiracy theory activists are found by searching for users whose messages contained the "5g" keyword. ## conclusion we examined dutch language twitter responses to the covid-19 pandemic from february until april of 2020. the overall public response appears to be substantially positive, mainly expressing trust, although differences across groups of users are present. the results demonstrate the effectiveness and necessity of monitoring social media platforms in order to gauge a nation's response to such large scale events. such findings were already documented in previous virus outbreaks, such as the mers outbreak in 2015 @xcite . the trend of the most discussed topics changes from talking about covid-19 in europe, to focusing more on the economy and adjustments of living styles, such as working from home (rq1). while different user groups mostly appear to discuss the same major issues (e.g., the development of the pandemic in the country, the measures adopted by the government, and the impact on the economy), there are differences in their priorities and the specific topics they emphasize. these differences seem to reflect the interests, the role in society, and the stance of the users during the covid-19 pandemic (rq2). emotional differences were also observed between the user groups, both in the overall emotional content as well as their dominant emotions. the dominant emotions, like the topics, reflect the roles of the groups, with politicians and government and health organization inspiring trust, tv/radio news outlining the sadness of the situation, the general public being worried but also feeling assured by the "people in charge", and some indication of mistrust by the users concerned with the conspiracy theories (rq3). future work will focus on three directions: first, we want to investigate the topics and emotional reactions of more fine-grained categories of users by targeting different professions (e.g., teachers, doctors, nurses, musicians, entertainers, among others); second, we want to investigate if the emotional response is different across geographic areas of the country that have been affected differently by the covid-19 outbreak. for instance, the noord brabant province was one of the major outbreak areas, while the groningen province was minimally affected. we thus expect that messages from noord brabant should be characterized by more negative emotions than those from the groningen area. finally, in future research it would be useful to run similar studies on datasets in other european languages, in order to allow a comparison of how different national situations and governmental responses impacted public perception of the early stages of the pandemic.