An In-depth Review of Privacy Concerns Raised by the COVID-19 Pandemic
Jiaqi Wang
Penn State University
jqwang@psu.edu

Abstract
COVID-19 has hugely changed our lives, work, and interactions with people. With more and more online activities, people are easily exposed to privacy threats. In this paper, we
explore how users self-disclose on social media and privacy
concerns raised from these behaviors. Based on recent news,
techniques, and research, we indicate three increasing privacy
threats caused by the COVID-19 pandemic. After that, we
provide a systematic analysis of potential privacy issues related to the COVID pandemic. Furthermore, we propose aseries of research directions about online user self-disclosure
and privacy issues for future work as well as possible solutions.

Introduction
COVID-19 has spread across the world and affected how
people work, live, and interact with each other. People arerecommended or required to work remotely, quarantine athome, and keep social distance. Under these circumstances,people expect more interactions with others via social media
platforms, which has led to a huge increase of social media
usage (Holmes 2020). Based on a study (Kanter 2020) of
25,000 consumers across 30 markets published on April 3rd,
2020, WhatsApp has seen a 40% increase in usage; in theearly phase of the pandemic usage increases 27%, in midphase 41% and countries in the late phase of the pandemic
see an increase of 51%; Facebook usage has increased 37%.
China experienced a 58% increase in usage of local social
media apps including Wechat and Weibo. Another study of4500 Influenster community members, most of respondents
agreed that their social media consumption (72%) and posting (43%) have increased during the pandemic. Moreover,
TikTok, one of new social media platforms, was used by thelargest share of teenagers (48%), overtaking even Instagram(47%) from March, 2020 to April, 2020 (Perez 2020).
One possible reason is that people are searching for alternative approaches to interact with others to stay mentally
healthy. People generate content, comment content, forward
content, and communicate with others on social media platforms. To increase a sense of intimacy with others, people share details of their lives with text, pictures, videos,
Copyright © 2021, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.

live video streaming, etc. To a great extent, the content canreveal personal private information including age, gender,
location, race, etc. Compared with interactions in the realworld, self-disclosure information can more easily be propagated, searched, saved, and even processed on social media. The increasing and more abundant self-disclosure may
cause unpredictable and unacceptable privacy disclosure tousers online. Furthermore, a recent research shows that people’s mental health problems are prevalent because of social
media exposure (Gao et al. 2020) itself, which means the expected results might be on the contrary to the mental healthcure.
However, the pandemic is changing people’s sensitivity and attitude to privacy including what and how personal information can be disclosed (Nabity-Grover, Cheung,and Thatcher 2020). Discussion about COVID-19 may include basic personal information, travel schedule, test results, symptom description, and medicine in use. These actsof self-disclosure reveal a lot of sensitive information thatpeople are not willing to share previously (Kordzadeh andWarren 2017). For example, health status and detailed description of individual body information are shared to ask
for comparison, suggestions or pre-diagnosis. Some communities even encourage people to share more personal information related to COVID-19 in the name of society responsibility without clarifying the boundary of gathered information and how to use the collected data. Based on theobservation, users would sacrifice personal information toa unprecedented degree to help the society back to the expected normal status. Recent work (Blose et al. 2020) provides early evidence that the situational factors caused by
COVID-19 may affect people’s self-disclosures and privacy
calculus. There is another issue we need to pay attention
to. Along with the COVID-19 pandemic, 2020 the UnitedStates presidential elections started from February and ends
in November. Noting that the date when United States officially declared the COVID-19 pandemic as a national emergency is March 13 and the first statewide ”stay-at-home” order was issued at California is March 16. That time is approximately only one month later than the early voting in
February. During the whole process of the presidential election, people are isolated at home and keep social distancein essential activities at most time. People have participatedextensively in political discussions, and actively engaged in

Data

Data
Processing

User

Data
Government

Data

Data
Sharing

Data

Unknown

Order &
MonitorSocial Media
Restrictions andMass Surveillance

Changing Users’ Behaviors

Multiple Information Aggregation

Figure 1: A Systematic Overview of Privacy Threats from Multiple Domains Related to the COVID-19 Pandemic
social media pushed by a highly divisive environment. Thisis likely linked to users disclosing sensitive information including but not limited to political stand, home address, andfamily relative information. The potential privacy harms tousers in the context of political debates have been studied
before (Rubinstein 2014). However, this election has introduced even additional situational factors, as it happened in
the middle of a pandemic.
Information sources across multiple social media may
cause serious user privacy issues and unclear selfdisclosures under the chaotic interactions with natural andsocial environment. Advanced machine learning and data
mining techniques investigate non-obvious relationships andsearch hidden data patterns, which can provide insights tothe data owners and external parties for unknown analysis(Chamikara et al. 2020).
In the following, we first summarize and analyze emerging privacy threats triggered by or enhanced by the COVID19 Pandemic. Based on our findings, we provide a high-levelcomprehensive analysis of privacy from multiple domains,propose related potential research directions,and conclude
implications for future online public privacy in crisis.. Finally, we discuss possible solutions of proposed researchquestions.

Increasing Privacy Threats due to the
COVID-19 Pandemic
Mass Surveillance
There is an ongoing public conversation about whether andunder what circumstances the United States should embrace a surveillance program for COVID-19 (Ram and Gray2020). Here, we focus on what tools the government andcompanies are leveraging from the phenomenon perspective.
There is increasing surveillance over people’s daily behaviors from the government and companies during the COVID-

19 pandemic in the name of monitoring and tracing the virus
spread (Hussein et al. 2020). Many countries and companies are leveraging people’s personal data (location, bodytemperature, facial information, etc.), which is collected bycell phones, traffic cameras, and other sensors, to track human mobility, identify individuals with risk, and monitor the
disease spread (Singer and Sang-hun 2020). In the United
Kingdom and India, smart city infrastructure has been reused to monitor the people’s social distance. In China, people can download a cell phone application that can tellwhether they have been exposed to COVID-19 by analyzing the collected location data and local infection situation(BBC 2020). In the United States, Apple and Google provided a contact tracing application for their mobile users aswell with bluetooth specification (Apple and Google 2020a)
and cryptography specification (Apple and Google 2020b).
However, as a key part of the extension of the surveillancestate, researchers stated that the anonymized data is not always anonymous and location data can exacerbate inequality. (Frith and Saker 2020).

Data Usage across Multiple Platforms
During the COVID-19 pandemic, people spent extensive
time online communicating, generating content, and engaging in other activities. With the development of data science
techniques, people have more computational power and various channels to collect, process, and share data. There havealready a lot of released open datasets focusing on different
aspects related to the COVID-19 (Blose et al. 2020; Chen,
Lerman, and Ferrara 2020; Pepe et al. 2020; Cohen et al.
2020; Cheng et al. 2020; Dong, Du, and Gardner 2020).
Many social media platforms provide APIs for people to acquire data, such as Twitter 1 and Reddit 2 . Those APIs lower
1
2

https://developer.twitter.com/en/docs/twitter-api
https://www.reddit.com/dev/api/

the barrier to access social media data. However, we can not
fully prevent malicious usage of the collected data.
At the same time, more digital records and accounts
containing sensitive information are being created online,
for example, online shopping accounts (Brough and Martin 2020) and other services that are brought online. Online
users may not be fully aware of the fact their private information can be collected, shared, and used in an unexpected
way (Malandrino et al. 2013). Many users may have morethan one accounts on social media. How to measure privacy
disclosure score based on the information across multiplesocial networks has been discussed (Aghasian et al. 2017)
extensively. Zola et al. explored a cross-source cross-domain
sentiment analysis with training data from Amazon and Tripadvisor and testing on the data from Facebook and Twitter(Zola et al. 2019).

Change of Individual Privacy Calculus
Another observed phenomenon and potential concern isthe change of individuals’ perception to self-disclosure andprivacy. Individual-level behavior during the pandemic is
a result of voluntary and government-enforced behavioral
change (Farooq, Laato, and Islam 2020). From the individual perspective, people are calibrating their behavior between information acquisition and privacy loss. Users may
have different attitudes and sensitivity to their privacy andself-disclosure during the pandemic (Fahey and Hino 2020).
People would more easily sacrifice their private health status
information to get suggestions, pre-diagnosis, or contributeto what the government appeals during the COVID-19 pandemic, especially in Asia (Cha 2020). Discussing personal
health status, symptom, and test results on social media has
become more common. Governments and companies provide convenient tools for people to update their personal information and implicitly convince people that the behaviors
are a contribution to the public good (Nabity-Grover, Cheung, and Thatcher 2020). However, to my best knowledge,
there are not enough official files to remind people about individual privacy issues or broadcast basic knowledge of data
usage for people during the COVID pandemic.
A systematic overview of privacy issues from different aspects during the COVID-19 Pandemic is shown in Figure 1.

Post-pandemic Potential Privacy Risks
Over-collected Data Abuse
The COVID-19 pandemic has promoted the developmentof e-commerce, online education, social media platforms,
smart phone applications, and related virtual service. Dueto the health emergency, many countries relax the regulation
restrictions or cooperate with companies to put the public
security in the first place by collecting and analyzing data
to support governmental prevention decision making. The
governments could leverage contact tracing information tomonitor and analyze citizens’ behaviors, e.g. LGBT people
identification in South Korea (Fahey and Hino 2020). Somecountries will put pressure on their companies to release
the collected data and provide data analysis on the involved

users. The European Commission has invited telecommunications companies to make their metadata available (Turner2020).
Tech companies, including Instagram, Twitter, Facebook,and etc., can abuse this detailed data sets of individually, by
selling, processing it to derive sensitive information, or sharing it inappropriately. Relying on powerful computational
resources such as GPU clusters, a huge amount of data, andadvanced data processing techniques, users behaviors can be
described, modelled, and predicted accurately without any
consideration for users’ privacy. For example, an example
of user behavior identification and prediction across multiple social media platforms is shown in Figure 2. Moreover,people share content via text, pictures, video, live streaming,and other formats, which can provide comprehensive information of users. Online interactions, e.g., “Follow”, “Hashtag”, “Mention”, “Reply”, can even reveal users’ friends
and relatives and create their social network structure. Thatwould cause other related users’ the privacy loss and overdisclosure and the propagation of the threat across the wholesocial media.
SocialMedia 1

Image 1

Information 2

SocialMedia 2

Image 2

RealUser Identity Information 3

SocialMedia 3

Information 1

InferredUser Identity
Image 3

Multiple Social Media

Figure 2: Users Potential Privacy Risks: User Identity Inference based on Multiple Social Media. For each social media,
one user would self-disclose part of personal information,for example, Information 1, Information 2, and Information
3. According to the disclosed information, one user can betreated as fuzzy image with released and limited inferred information on one social media, for example, Image 1, Image
2, and Image 3. However, given multiple social media dataof one user and advanced across-platform data processing
techniques, data can be aggregated to infer a more accurate
user identity with detailed personal information.

Public Privacy Concern and Social Trustworthiness
As the COVID-19 pandemic carries on, debates and laws
surrounding surveillance capabilities are at the forefront ofmany minds (ROSS 2020). However, a majority of Americans said that they were concerned about how their personal
data would be used by data collectors and they knew extremely little about the laws or regulations to protect theirdata privacy(Auxier 2020). Many governments gather oreven over-collect people’s data during the pandemic via different approaches. There is a great possibility that they will
not delete the collected personal data or even continue collecting the data without informing users. Another survey result in (Auxier 2020) shows that 69% U.S. adults thought
they should have the right to have the medical data permanently deleted after necessary and legal usage. While peo-

Table 1: Possible Research Directions and Questions about Privacy Issues and Self-disclosure related t Crisis On Social Media
Research Directions

Self-disclosure Interaction and Propagation

Public Privacy Concern and Attitude Tracing

Mental Health in the COVID-19 Pandemic

Prevention, Prediction, and Protection

Research Questions• How and to what extent users’ self-disclosure behaviors can affect otherrelated users on social media?
• How the self-disclosure behaviors propagate on the social media?
• To what extent the crisis would affect the user self-disclosure behaviors?
• How to find the balance point between the privacy preserving and selfdisclosure to get enough and appropriate information in crisis?
• How to quantify self-disclosure across multiple social media and provide
a varying evaluation considering situational factors?
• How to trace the public privacy attitude change to their current status?
• How to design an appropriate data-driven mechanism and regulation togather appropriate data and decrease the public privacy concern?
• How to model the complex and dynamic observations considering users’
privacy concern, users’ behaviors, and the pandemic crisis?
• How to find a balance between keeping mental health and privacy during
the pandemic?
• How the mental health status, self-disclosure, and privacy concern affecteach other? Certain self-disclosure can help users keep a good mental health,while it takes private concerns to users as well.
• During the health emergency crisis, considering users with different physical health status, would there be any differences of their mental health andonline behaviors?
• How to design a comprehensive mechanism to prevent over self-disclosure
and privacy-disclosure according to complicated scenarios in crisis?
• How to predict public behavior and provide appropriate suggestions withlimited access of data during the pandemic?
• How to protect users’ provided data, protect the stability on social media,
and establish social trust?

ple enjoy the benefit of pandemic tracking and controlling
via the data-driven approach, it also raises public concerns
for their individual privacy. Kye and Hwang argued thatthe government actions do have a huge impact on social
trust and government Trustworthiness. The temporal overdisclosed data and privacy data disclosure is gradually causing a stronger public privacy concern and challenging thegovernment social trust.

Potential Research about Pandemic-related
Privacy Issues on Social Media
Based on previous work and our discussion, we propose a setof related research directions (shown in Table 1) to understand and explore further privacy issues at time of COVID.
They include: (i) self-disclosure interaction and propagation; (ii) public privacy concern and attitude tracing; (iii)
mental health; (iv) prevention, prediction, and protection in
the COVID pandemic. For each research direction, we provide several related specific research questions in the table 1
as well for future exploration.

Conclusion
The COVID-19 pandemic has generated a lot of practicalproblems and research questions related to privacy issues inonline settings. In this paper, we describe how the COVID19 affects user behaviors on social media. After that, we discuss three increasing privacy threats due to the pandemic
including mass surveillance, data usage across multiple platforms, and change of people’s privacy calculus. Furthermore, we introduce possible privacy risk after the pandemic.
Finally, we propose a set of related research topics for further study. There could be several possible research directions: (i) appropriate and adaptive approaches to quantify
self-disclosure and privacy combining peoples’ comprehensive behaviors in multiple scenarios; (ii) mathematical andstatistical models of privacy and human behaviors rather thatcan complement data-driven approaches ; (iii) study the interactions between people’s awareness and sensitivity of privacy and self-disclosure considering the changes of environment. Different people may have different initial attitudes
towards their personal information and decide how muchinformation they feel comfortable to self-disclose. The exploration of the hidden relation between privacy attitudes,
self-disclosure behaviors, and the reaction got from the environment can help us understand humans’ privacy-related
behaviors better and provide comprehensive suggestions forprivacy-preserving mechanism design.

References
Aghasian, E.; Garg, S.; Gao, L.; Yu, S.; and Montgomery,J. 2017. Scoring users’ privacy disclosure across multiple
online social networks. IEEE access 5: 13118–13130.
Apple; and Google. 2020a. Contact Tracing BluetoothSpecification. URL https://www.blog.google/documents/
58/Contact Tracing - Bluetooth Specification v1.
1 RYGZbKW.pdf.
Apple; and Google. 2020b. Contact Tracing Cryptography
Specification. URL https://www.blog.google/documents/56/
Contact Tracing - Cryptography Specification.pdf.
Auxier, B. 2020.
How Americans see digital privacy issues amid the COVID-19 outbreak.
URL
https://www.pewresearch.org/fact-tank/2020/05/04/howamericans-see-digital-privacy-issues-amid-the-covid-19outbreak/.
BBC. 2020. China launches coronavirus ’close contact detector’ app. URL https://www.bbc.com/news/technology51439401.
Blose, T.; Umar, P.; Squicciarini, A.; and Rajtmajer, S. 2020.
Privacy in Crisis: A study of self-disclosure during the Coronavirus pandemic. arXiv preprint arXiv:2004.09717 .
Brough, A. R.; and Martin, K. D. 2020. Consumer Privacy
During (and After) the COVID-19 Pandemic. Journal of
Public Policy & Marketing 0743915620929999.
Cha, V. 2020. Asia’s COVID-19 Lessons for the West: Public Goods, Privacy, and Social Tagging. The Washington
Quarterly 1–18.
Chamikara, M. A. P.; Bertók, P.; Liu, D.; Camtepe, S.; andKhalil, I. 2020. Efficient privacy preservation of big data for
accurate data mining. Information Sciences 527: 420–443.
Chen, E.; Lerman, K.; and Ferrara, E. 2020. Covid-19:
The first public coronavirus twitter dataset. arXiv preprint
arXiv:2003.07372 .
Cheng, C.; Barceló, J.; Hartnett, A. S.; Kubinec, R.; andMesserschmidt, L. 2020. Covid-19 government response
event dataset (coronanet v. 1.0). Nature human behaviour
4(7): 756–768.
Cohen, J. P.; Morrison, P.; Dao, L.; Roth, K.; Duong, T. Q.;
and Ghassemi, M. 2020. Covid-19 image data collection: Prospective predictions are the future. arXiv preprint
arXiv:2006.11988 .
Dong, E.; Du, H.; and Gardner, L. 2020. An interactive webbased dashboard to track COVID-19 in real time. The Lancet
infectious diseases 20(5): 533–534.
Fahey, R. A.; and Hino, A. 2020. COVID-19, digital privacy, and the social limits on data-focused public health responses. International Journal of Information Management
55: 102181.
Farooq, A.; Laato, S.; and Islam, A. N. 2020. Impact
of online information on self-isolation intention during the
COVID-19 pandemic: cross-sectional study. Journal of
medical Internet research 22(5): e19128.

Frith, J.; and Saker, M. 2020. It Is All About Location:
Smartphones and Tracking the Spread of COVID-19. Social Media+ Society 6(3): 2056305120948257.
Gao, J.; Zheng, P.; Jia, Y.; Chen, H.; Mao, Y.; Chen, S.;
Wang, Y.; Fu, H.; and Dai, J. 2020. Mental health problems
and social media exposure during COVID-19 outbreak. Plosone 15(4): e0231924.
Holmes, R. 2020. Is COVID-19 Social Media’s Levelling Up Moment?
URL https://www.forbes.com/
sites/ryanholmes/2020/04/24/is-covid-19-social-mediaslevelling-up-moment/?sh=5fa080dd6c60.
Hussein, M. R.; Shams, A. B.; Apu, E. H.; Mamun, K. A. A.;
and Rahman, M. S. 2020. Digital Surveillance Systems for
Tracing COVID-19: Privacy and Security Challenges with
Recommendations. arXiv preprint arXiv:2007.13182 .
Kanter. 2020.
COVID-19 Barometer: Consumer
attitudes, media habits and expectations.
URL
https://www.kantar.com/Inspiration/Coronavirus/COVID19-Barometer-Consumer-attitudes-media-habits-andexpectations.
Kordzadeh, N.; and Warren, J. 2017. Communicating personal health information in virtual health communities: anintegration of privacy calculus model and affective commitment. Journal of the Association for Information Systems
18(1): 1.
Kye, B.; and Hwang, S.-J. 2020. Social trust in the midstof pandemic crisis: Implications from COVID-19 of South
Korea. Research in social stratification and mobility 68:
100523.
Malandrino, D.; Petta, A.; Scarano, V.; Serra, L.; Spinelli,R.; and Krishnamurthy, B. 2013. Privacy awareness about
information leakage: Who knows what about me? In Proceedings of the 12th ACM workshop on Workshop on privacyin the electronic society, 279–284.
Nabity-Grover, T.; Cheung, C. M.; and Thatcher, J. B. 2020.
Inside out and outside in: How the COVID-19 pandemic affects self-disclosure on social media. International Journalof Information Management 55: 102188.
Pepe, E.; Bajardi, P.; Gauvin, L.; Privitera, F.; Lake, B.; Cattuto, C.; and Tizzoni, M. 2020. COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific data 7(1): 1–7.
Perez, S. 2020. TikTok Engagement Among Kids Surges
During the Pandemic. URL https://www.marketingcharts.
com/demographics-and-audiences/teens-and-younger113749.
Ram, N.; and Gray, D. 2020. Mass surveillance in the ageof COVID-19. Journal of Law and the Biosciences 7(1):lsaa023.
ROSS, C. R. 2020. Will we give up privacy for security
after Covid-19? URL https://www.statnews.com/2020/04/
08/coronavirus-will-we-give-up-privacy-for-security/.
Rubinstein, I. S. 2014. Voter privacy in the age of big data.
Wis. L. Rev. 861.

Singer, N.; and Sang-hun, C. 2020.
As Coronavirus
Surveillance Escalates, Personal Privacy Plummets.
URL
https://www.nytimes.com/2020/03/23/technology/
coronavirus-surveillance-tracking-privacy.html.
Turner, J. 2020. Privacy vs. Security in the Post-Pandemic
World. URL https://www.natlawreview.com/article/privacyvs-security-post-pandemic-world.
Zola, P.; Cortez, P.; Ragno, C.; and Brentari, E. 2019. Social
media cross-source and cross-domain sentiment classification .