Data from a global survey held in March 2020 revealed that almost a third of responding Gen Z internet users worldwide felt that social media companies should provide live-streams of events during the coronavirus crisis. However, only ** percent of Baby Boomer respondents thought the same.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Based on survey results from **********, daily usage of WhatsApp and Instagram increased the most due to the coronavirus (COVID-19) outbreak in Finland. WhatsApp usage among Finns increased by **** percent compared to the period before the COVID-19 restriction measures were put in place. While most social media platforms increased their popularity, daily usage of Facebook, internet forums, blogs, and LinkedIn decreased during the pandemic.

Data from a global survey held in March 2020 revealed that ** percent of responding internet users worldwide felt that social media companies should help neighbors and local communities to connect with each other during the coronavirus crisis, though this varied somewhat by country. More than half of survey participants from the UK and the Philippines were in favor of this, yet only ** percent of Japanese respondents thought the same. Over two thirds of global respondents also thought that it was the responsibility of social media platforms to provide fact-checked content to help people cope with the outbreak.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

The survey was adapted from prior studies. It was validated through PLS-SEM measurement model

Please cite the following paper when using this dataset:

N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

Abstract

The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

The following is a description of the attributes present in this dataset - Post ID: Unique ID of each Instagram post - Post Description: Complete description of each post in the language in which it was originally published - Date: Date of publication in MM/DD/YYYY format - Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API - Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API - Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

Open Research Questions

This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

• How does sentiment toward COVID-19 vary across different languages?
• How has public sentiment toward COVID-19 evolved from 2020 to the present?
• How do cultural differences affect social media discourse about COVID-19 across various languages?
• How has COVID-19 impacted mental health, as reflected in social media posts across different languages?
• How effective were public health campaigns in shifting public sentiment in different languages?
• What patterns of vaccine hesitancy or support are present in different languages?
• How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?
• What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?
• How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?
• What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

This study utilized a non-experimental descriptive survey design to examine social media information-seeking behaviors during the COVID-19 outbreak, particularly during lockdown periods. The objectives were to describe perceptions of COVID-19 information on social media, explore the platforms used during lockdown, identify groups of connections on social media, and determine if platform use varied based on connected groups. To gather data on information-seeking behaviors, an online survey was administered via Qualtrics, reaching 1,048 respondents in the United States through non-probability opt-in sampling. The survey included the perceptions of the information availability scale (Woolley & Propst, 2005) and an information-seeking behavior scale (Timmers & Glas, 2010) the information availability scale (Woolley & Propst, 2005), and some researcher-adapted Likert-type scales. The results revealed that more than 70% of respondents felt overwhelmed while searching for COVID-19 information, encountered difficulties in accessing and interpreting additional information, and sometimes even avoided news about the pandemic. Among social media platforms, Facebook, Instagram, and Twitter were the most popular for obtaining COVID-19 information. Notably, Facebook emerged as the most widely used platform during lockdowns. Furthermore, respondents primarily utilized Facebook to connect with friends and family during the pandemic, and those with larger social networks tended to access social media platforms more frequently. These findings highlight the significant role of Facebook in disseminating reliable information during the COVID-19 pandemic. They also emphasize the importance of implementing strategies to help individuals navigate the overwhelming amount of information, including misinformation, present on social media platforms, particularly during times of crisis. It is worth noting that there is limited generalizability due to US-centric sample.

The current dataset contains Tweet IDs for tweets mentioning "COVID" (e.g., COVID-19, COVID19) and shared between March and July of 2020.Sampling Method: hourly requests sent to Twitter Search API using Social Feed Manager, an open source software that harvests social media data and related content from Twitter and other platforms.NOTE: 1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset. 2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc). 3) This dataset, like most datasets collected via the Twitter Search API, is a sample of the available tweets on this topic and is not meant to be comprehensive. Some COVID-related tweets might not be included in the dataset either because the tweets were collected using a standardized but intermittent (hourly) sampling protocol or because tweets used hashtags/keywords other than COVID (e.g., Coronavirus or #nCoV). 4) To broaden this sample, consider comparing/merging this dataset with other COVID-19 related public datasets such as: https://github.com/thepanacealab/covid19_twitter https://ieee-dataport.org/open-access/corona-virus-covid-19-tweets-dataset https://github.com/echen102/COVID-19-TweetIDs

During national crises, political elites often rally ’round the flag, promoting a central message to restore unity and calm the public. COVID-19 provided a crisis. But did elites rally? The pandemic occurred at a point of extreme polarization in the United States, which threatens the potential for a rally. In this note, we argue that messaging about masking during COVID-19 offers an opportunity to test the comparative effects of a rally versus polarization. To do so, we use a unique measure: visual public communication by Members of Congress (MOCs). We extract 340,000 images from Congressional Twitter and Facebook accounts and employ supervised machine learning methods to identify when MOCs post images of people wearing masks. We find more evidence of rally effects and polarization. Trump’s actions are especially important: while Trump-loyal Republicans are less likely to post masks, all Republicans increase posting masks after Trump first appears wearing a face mask.

This data set contains information on the number of visits and new visitors to the NYC HOPE website (https://www1.nyc.gov/nychope/site/page/home). The website provides information on domestic and gender-based violence, including resources and services that are available in New York City.

As of March 2020, social media users in the United States were staying online more. According to a survey of U.S. social media users, **** percent of respondents were using social media *** hours additional hours per day. A further **** percent used social media ** minutes to *** hour more than usual per day. Only *** percent of users were adding ******************** to their usage. Additional social media usage was a result of the coronavirus pandemic, which caused stay home orders and social distancing to be put in place in the country.

These datasets aim to provide an empirical report to understand the key predictors of Teachers’ Social Media Use during Corona Virus Disease 2019

This dataset is the supporting data for the paper Underneath Social Media Texts: Sentiment Responses to Public Health Emergency During 2022 COVID-19 Pandemic in China.This dataset is mainly used to analyze the data of weibo text and perform sentiment analysis. The data were obtained from Weibo, and the texts were crawled using a Python tool: Weibo crawler tool. The data contains time, text content, user address, etc. Subsequently, Cleaned weibo data was obtained after cleaning operation in Excel. According to the improved Chinese sentiment lexicon, the sentiment analysis tool was used to analyze the text for sentiment analysis, to derive the main sentiment and sentiment scores, and the result file is Sentiment analysis results. Finally, ADF and KPSS analysis tools were used to analyze the stability of sentiment scores in different cities.The weibo text and sentiment analysis results data in the dataset are in .xlsx format, and the rest of the tools are Python code.Crawled data is limited by time, specific search terms and other restrictions, different operation time and terms may lead to differences in the data.

In these polarized and challenging times, not even perceptions of personal risk are immune to partisanship. This article introduces results from a new survey with an embedded social media experiment conducted during the first months of the COVID-19 pandemic in Brazil. Descriptive results show that pro-government and opposition partisans report very different expectations of health and job risks. Job and health policy have become wedge issues that elicit partisan responses. The analysis exploits random variation in the survey recruitment to show the effects of the president’s first speech on national television on the perceived risk and the moderating effect of partisanship. The article presents a framing experiment that models key cognitive mechanisms driving partisan differences in perceptions of health risks and job security during the COVID-19 crisis.

As the COVID-19 pandemic restricted individuals to their houses for a substantial amount of time, people took to the virtual world to stay connected with their peers, family and friends. Likewise, news channels and other forms of electronic media also witnessed a steep rise in viewership all across the globe. That being said, social media has led to adverse impacts on the mental health of individuals through addiction, stress, anxiety, depression and post-traumatic stress syndromes.

The primary objective of this data is to analyze both the positive and negative effects of social media usage on individuals during an unprecedented global lockdown. Existing literature has found significant connections between the use of social media and mental health during extensive periods of lockdown (Swarnam. S., 2021; Pragholapat, A., 2020., Hong, W. et al., 2020). This dataset is used to understand the extent of depression and anxiety experienced by persons restricted to stay-at-home confinements ...

Data is about the impact of social media on research competencies of the allied academicians' research completion during pandemic.

This dataset presents a large-scale collection of millions of Twitter posts related to the coronavirus pandemic in Spanish language. The collection was built by monitoring public posts written in Spanish containing a diverse set of hashtags related to the COVID-19, as well as tweets shared by the official Argentinian government offices, such as ministries and secretaries at different levels. Data was collected between March and August 2020 using the Twitter API. In addition to tweets IDs, the dataset includes information about mentions, retweets, media, URLs, hashtags, replies, users and content-based user relations, allowing the observation of the dynamics of the shared information. Data is presented in different tables that can be analysed separately or combined. The dataset aims at serving as source for studying several coronavirus effects in people through social media, including the impact of public policies, the perception of risk and related disease consequences, the adoption of guidelines, the emergence, dynamics and propagation of disinformation and rumours, the formation of communities and other social phenomena, the evolution of health related indicators (such as fear, stress, sleep disorders, or children behaviour changes), among other possibilities. In this sense, the dataset can be useful for multi-disciplinary researchers related to the different fields of data science, social network analysis, social computing, medical informatics, social sciences, among others.

Social media has been vital for consumers, suppliers, workforce and partners to remain engaged amidst this exogenous event of COVID-19.
This thematic research report takes an in-depth look at the theme of Social Media and its impact on travel and tourism during COVID-19 affecting super-national organizations, DMO’s, airlines, lodging providers, cruise operators and travel intermediaries. This report analyzes the major impacts that may become longstanding and then presents an array of case studies demonstrating the creative and innovative ways companies and organizations have acted during this time.
“Social media has most openly been utilized as a tool for travel businesses and DMO’s to maintain contact with consumers worldwide – to generate wanderlust and look towards recovery when travel is once again possible. Even though the battle with COVID-19 is now beginning to lessen and restrictions are easing, it is clear there will be long-standing impacts on consumer behavior and social media is one of the major themes that will drive future changes”. – Johanna Bonhill-Smith, Travel & Tourism Associate Analyst, GlobalData. Read More

Huge citizens expose to social media during a novel coronavirus disease (COVID-19) outbroke in Wuhan, China. We assess the prevalence of mental health problems and examine their association with social media exposure. A cross-sectional study among Chinese citizens aged≥18 years old was conducted during Jan 31 to Feb 2, 2020. Online survey was used to do rapid assessment. Total of 4872 participants from 31 provinces and autonomous regions were involved in the current study. Besides demographics and social media exposure (SME), depression was assessed by The Chinese version of WHO-Five Well-Being Index (WHO-5) and anxiety was assessed by Chinese version of generalized anxiety disorder scale (GAD-7). multivariable logistic regressions were used to identify associations between social media exposure with mental health problems after controlling for covariates. The prevalence of depression, anxiety and combination of depression and anxiety (CDA) was 48.3% (95%CI: 46.9%-49.7%), 22.6% (95%CI: 21.4%-23.8%) and 19.4% (95%CI: 18.3%-20.6%) during COVID-19 outbroke in Wuhan, China. More than 80% (95%CI:80.9%-83.1%) of participants reported frequently exposed to social media. After controlling for covariates, frequently SME was positively associated with high odds of anxiety (OR = 1.72, 95%CI: 1.31–2.26) and CDA (OR = 1.91, 95%CI: 1.52–2.41) compared with less SME. Our findings show there are high prevalence of mental health problems, which positively associated with frequently SME during the COVID-19 outbreak. These findings implicated the government need pay more attention to mental health problems, especially depression and anxiety among general population and combating with “infodemic” while combating during public health emergency.

Supplementary files for article "Beyond social media: The influence of news consumption, populism, and expert trust on belief in COVID-19 misinformation"The COVID-19 pandemic was accompanied by an unprecedented influx of misinformation often with adverse impact on the effectiveness of institutional responses to the health crisis. However, relatively little is still known about the factors that may have facilitated the proliferation and public acceptance of misinformation related to the virus or to the government’s anti-pandemic measures, particularly in comparative perspective. Utilizing data collected by a representative cross-country survey (N = 5,000) in four countries led by populist leaders during the pandemic—Brazil, Poland, Serbia, and the United States—this study explores the links between three mutually interrelated factors, namely media usage across different platforms, affinity to populism, and trust in scientific expertise, and people’s beliefs in selected COVID-related misinformation. The findings show that preexisting attitudes, especially affinity to populism and mistrust in experts, are generally stronger predictors of people’s likelihood to endorse misinformation related to the pandemic than their news consumption patterns. Nevertheless, the analysis also indicates an important role played by exposure to specific media brands, particularly those promoting a skeptical stance toward preventive measures and COVID-19 vaccines, as well as messaging apps, which display stronger relationship with misinformation beliefs than social networking sites. The article concludes by discussing implications for practical efforts to combat misinformation, especially during a health crisis.©The Author(s), CC BY 4.0

Social media platforms have become integral tools in the conduct of foreign policy for many nations, including India. This dataset serves as a resource for analyzing ‘Social Media and India’s Foreign Policy: The Case Study of ‘X’ Diplomacy during the Covid-19 Pandemic.’ The data were collected through a web-based questionnaire distributed primarily to people aged 18 – 61 and above in India. A total of 171 valid data were collected from 17 states offering extensive geographic coverage and stored in Mendeley. The 15 contributor states are Goa, Maharashtra, Tamil Nadu, Gujarat, Delhi, Assam, Haryana, Jammu and Kashmir, Karnataka, Kerala, Punjab, Rajasthan, Tripura, Uttar Pradesh and West Bengal. It encompasses diverse question formats, including single-choice, multiple-choice, quizzes, and open-ended. The study underscores the opportunities and challenges of employing 'X' diplomacy in India's foreign policy. Thus, there were two hypotheses. First, India's effective use of 'X' diplomacy positively impacts public perception of India's foreign policy effectiveness. Second, India's adept use of 'X' diplomacy during the COVID-19 pandemic enhances its ability to manage and respond to the crisis effectively. This data shows public perception of the effective use of social media by the Government of India, particularly in the crisis situation. Data also highlight the significant change in India’s narrative through its ‘X’ diplomacy, effectively setting the narratives, public perceptions, and diplomatic strategies. This data can be fully utilized in the study of the significance of social media in India’s foreign policy, the role of social media like ‘X’ in the making of India’s foreign policy, how effective social media like ‘X’ was during the Covid-19 pandemic and how Indian government utilized social media like ‘X’ to delivered messages and to set the narrative in the international politics.