Collective Stress in the Digital Age

COLLECTIVE STRESS IN
THE DIGITAL AGE
Talha Oz
Computational Social Science
PhD Defense - 10/27/2020
Advisor: Andrew T. Crooks
Committee: William G. Kennedy, Trevor A. Thrall, Arie Croitoru
Department of Computational and Data Sciences
George Mason University

OUTLINE
Introduction
Studies
1. Measuring Work Stressors
2. Impact of COVID-19 on Work
3. Attribution of Blame on #FlintWaterCrisis
Conclusion
2
Intro Study-1 Study-2 Study-3 Conclusion

3
STRESS PROCESS
Stressors: life events, disasters,
discrimination, oppressions, etc.
Social contexts: socioeconomic
status, social and political
structures, culture, beliefs, values,
life history
Perception: appraisal of stressors
Coping/response: social and
personal resources
Outcomes: mental & physical health,
org. productivity, social systems

4
Macro
Scope/SocialContext
Micro
Chronic /
Continuous
The Stress ContinuumSudden /
Discrete
Systemic racism in a
country
Toxic job climate in
an organization
Chronic diseases
(e.g., diabetes)
Flint water crisis
Daily hassles (e.g.,
traffic jams, waiting
in the line)
Life events (e.g.,
death of spouse)
Downsizing of a
company
9/11 terror attacks
COVID-19 Pandemic
STRESSORS UNIVERSE

STRESS IS COMMON, COSTLY, & TRENDING
50
55
60
65
70
75
80
85
2017 2018 2019 2020
Percentage
% of Americans reporting that the future of the nation is
a significant source of stress (APA 2020)
5
$1 Trillion / year
1 Million off / day
2008: +46K lives lost

RESEARCH MOTIVATION
ARjats.cls April 18, 2020 9:14
Business
Psychology
Education
Political
science
Sociology
0
50
100
150
200
20062004 2008 2012 20162010 2014
Year
Numberofpublications
Figure 1
Number of computational social science publications by year—2003–2016—across four scholarly disciplines.
Before we present our results for the discipline of sociology, we provide an overview of the
evolution of computational social science across a broader set of fields. Figure 1 is a time series
graph that describes the number of publications within five scholarly disciplines where scholar-
ship mentioned the terms computational social science or big data between 2000 and 2016. This
figure should be taken as a rough approximation of the field, given that individual articles were
not reviewed by human coders to confirm that they are on the subject of computational social
science. Still, several things are noteworthy about this figure. First, there has been a remarkable
Number of CSS publications by year
(Edelmannetal.2020)
Social Sciences
CSS
Computational
& Data
Sciences
Collective
Stress
ResearchCrisis
Informatics
Traditional
CSR
CSS of
CSR
6
Important &
Gaps & Calls
The Digital Age
(Object change)
Big Data
(New Sources)
Computational
methods

RESEARCH QUESTIONS
• How to model work stressors with digital exhaust & make use of the models effectively?Study-1
• How do employees adapt their communication when they are forced to WFH (and while most
childcare facilities are closed)?Study-2
• How do people attribute responsibility online when the government fails at all levels?Study-3
7
Study Stressors Coping/Responses Study Type (Theory-informed CSS studies)
# 1 Toxic job culture Quit / live with it / fix Position (why) paper & Strategy (how) proposal
# 2 COVID-19 (WFH) Adjust work behavior Work ICT metadata analysis (extension of #1)
# 3 Unclean tap water Blame the responsible agents Social media data analysis (meta + content)

DIGITAL TRAILS OF WORK STRESSORS Oz (2020)

The health effects of work stressors vs secondhand smoking exposure (Goh et al. 2015)
BACKGROUND
9
Counseling
Services
Resilience Training
Wellness Programs
Organizational Stressors
Intervention Methods

STATE OF THE ART
Measurement Methods
Surveys
(+) Depth & breadth
(–) Biases (esp. for obj. stressors)
(–) Costly (Employee time)
(+) Targeted: when, what, whom
Other methods
Diaries; Trained observers; Employee
handbooks; Job listings
Digital trails of enacted events
Stressor appraisal
Employers conduct survey (quarterly)
Stressors “in the environment”
Employees report on objective stressors
Objective reality or just a shared perception?

RESEARCH QUESTIONS
Digital trails/exhaust = Metadata (no content)
Email: MS-Exchange / G-workspace
Scheduled meetings: Calendar
Instant Messaging: Slack / MS-Teams
Unscheduled calls: Skype / Zoom
Sensors & Smart ID Badges
Other collaboration: Dropbox & Github
RQ1: What work stressors can be diagnosed from the digital exhaust?
RQ2: How to model and make use of them most effectively?

EXAMPLE: MODELING A STRESSOR INDICATOR
Let’s go over an example: Work-life Balance (WLB)
1. How to model it? Two measurable items: weekend time, weekday OBH
2. Which data sources?
3. How much / how long data do I need?
4. How do I aggregate the data over time and across people?
5. What is an acceptable range and what is critical?
6. How to report the score to executives, team leaders, and to employee itself?
7. What custom analysis can be done? E.g., who drives communication outside work?
For each stressor model, these decisions need to be made clearly.

STRUCTURAL / NETWORK STRESSORS
Network construction
Combine (sum) all streams (over a time period)
unscheduled calls + messaging + scheduled meetings + f2f + …
A common unit across different media
Attention minutes
Group-level
N/A at individual level
Stressors
Diversity & Inclusion
Attribute assortativity
(Intra-) Team cohesion / engagement
Time it takes for a new hire to move from periphery to core
(Inter-) Team silo-ness
Information bottlenecks

TIME MANAGEMENT STRESSORS
Meetings not start/end on time (calendar)
People join/leave times (room sensors; Zoom)
Beyond Analysis
Send a nudge to habitually late joiners before meetings
Short-notice meetings
Δ = Meeting-start-time – invitation-sent-time
Postponed / canceled / double-booked
Count/ratio
Interruption stressor
Allocate time to focus
Interruption stressor
e-mail
meetingEmployee-1
Employee-2

RQ2: ONCE MODELED, YOU CAN…
Check against acceptable range: safe/critical
Slice & dice (by team, level, job function, country, etc.)
Benchmark: convey the local norms
Intervention/policy effectiveness: A/B tests
Trend analysis – pre/post analysis
Retrospective cohort analysis: quit due to distress?
Targeted surveying
15
Overwork by Country
(A multinational company)

Proposed strategy allows
Objective measurement
better than state-of-the-art (SOTA)
Unobtrusive
No cost to employee
Automated (scalable)
Quick decision making
Benchmarking
is by itself an intervention
Targeted surveys
increases the effectiveness of SOTA
Controlling surveys
Convergence/divergence of methods
CONCLUSION
Limitations & Challenges
Ethics & Privacy
Opt-in; transparent
No content
No individual-level reporting to managers
Report groups of 5+ people
Tech solutionism trap
Not everything is observable

THE IMPACT OF COVID-19 ON INTRA-FIRM
COMMUNICATION: BAU VS WFH
Oz and Crooks (2020)

BACKGROUND
COVID-19 (March 2020)
+34% of US employees started to WFH
Employers ask
What WFH policies do I need: can data inform me?
Research questions
What happens when f2f is no more an option? Why?
What might be the (heterogeneous) effects of COVID-19?
Formed 10 hypotheses & tested
18

DATA & METHOD
A Tech Company
N=35 (ET-office = 26, PT-office = 9)
Five months: 2020-01-06 to 2020-05-31
Pre (BAU): 01/06 - 03/15 (10 weeks)
Post (WFH): 03/16 – 05/31 (11 weeks)
Communication Metadata
Meetings (Calendar)
Messaging (Slack)
Employee metadata
Office, primary team, gender, is-manager
19
• Method
• Every event creates weighted directed links
• (attn giver, attn receiver, attn minutes)
• Messages:
!"#$%&#!' %!""&(! )*$#$+( #$%!
# -. *!/!$0!*"
• Meetings:
%!!#$+( #$%!
# -. 1&*#$/$1&+#" 2 3
• Aggregate pairs weekly
• (week1, p1, p2, messaging _am)
• (week1, p1, p2, meeting_am)

HYPOTHESES & FINDINGS
(H1a) CMC increases within-office but not much across offices
(H1b) and this increase is driven by within-team communication
20
PT Office:
A, B
ET Office:
C, D, EPre/BAU:
Post/WFH: A B C D E
(1 team) (3 teams)

(H2) Much of the within-team f2f move to Messaging rather than Meeting (✓)
Maintains connection (#channels, chat history)
Context-rich (emojis)
Coordination (MPDMs, apps, bots)
Quick questions/clarifications on IM is faster than dialing in
Negotiates availability (convenience asymmetry) –even better than shared space
21
Within-team comm. BAU (min) WFH (min) Change (min) Change ratio
Meeting 212.41 219.19 +6.78 +3.19 %
Messaging 197.57 245.78 +48.21 +24.40 %
87.7% =
%!""&($+( $+/*!&"!
#-#&4 $+/*!&"!

(H3) Employees setup extra (short) meetings and (H4) remove long meetings (✓, ✓)
Switch media: complicated, misunderstanding, too much typing
Media richness theory
Inefficient meetings: attentional contract
Zoom fatigue
22
Pre (BAU) Post (WFH) Change (count) Ratio (%)
<30 mins 4.64 5.83 1.19 +25.53 %
30 mins 12.70 17.31 4.62 +36.35 %
31-45 mins 1.11 1.76 0.64 +58.01 %
46-60 mins 9.76 15.44 5.67 +58.11 %
1-1.5 hours 1.49 1.26 -0.23 -15.21 %
1.5-2 hours 0.53 0.32 -0.21 -40.23 %
2-24 hours 1.85 0.76 -1.09 -59.02 %

23
(H5) In WFH, employees communicate outside regular hours more (✓)
Flexibility & autonomy prevails!
Schools & childcare facilities closed L
(H6) Women communicate (work?) more in the after-hours (✓)
Take on more childcare & household work (9h vs 21h)
OBH Pre (BAU) Post (WFH) Change Ratio
F 10.17 31.34 +21.17 208.26 %
M 11.66 21.04 +9.37 80.38 %
IBH
F 66.14 157.89 +91.75 138.72 %
M 54.32 105.11 +50.78 93.48 %
(H5-6) Outside/Inside business hours messaging minutes (OBH/IBH).

24
(H7) Cross-level communication increases more than that of same-level (✓)
More supervisor support is needed when WFH
Managers are valuable contacts (Tie Decay Theory)
From – To (is
manager?)
Pre (BAU)
(minute)
Post (WFH)
(minute)
Change (minute) Change (Ratio)
N - N 153.93 179.76 25.83 16.78 %
N - Y 88.02 114.25 26.23 29.80 %
Y - N 136.04 181.77 45.73 33.61 %
Y - Y 234.22 245.60 11.37 4.86 %
Meet w/ Pre (min) Post (min) Change Ratio (%)
N - N 117.39 131.03 13.64 11.62 %
N - Y 122.23 145.33 23.11 18.91 %
Y - N 110.27 187.19 76.92 69.76 %
Y - Y 184.91 239.10 54.19 29.31 %

(H8) Message turnaround would be shorter in WFH (✓)
Greater level of telepressure when WFH
(H9) Respond to their managers even quicker (X)
flexibility stigma
25
From – To
is manager
Pre (min) Post (min) Change
(min)
Ratio
N - N 17.73 12.93 –4.80 –27.07 %
N - Y 20.48 17.29 –3.18 –15.55 %
Y - N 16.59 14.05 –2.54 –15.32 %
Y - Y 19.73 19.11 –0.63 –3.17 %
Response to managers is faster but to non-managers is even faster

CONCLUSION
Findings supported the hypotheses based on the theories of
Computer mediated communication (Nardi and Whittaker 2002; Whittaker 2003)
Zoom fatigue (Tarafdar 2019; Thompson 2020); Telepressure (Barber and Santuzzi 2015)
Tie decay choices (Kleinbaum 2017); Remote supervision (Fonner 2010)
Flexibility stigma (Chung 2018); Household management (Ramey and Ramey 2009)
Discussions
WFH might cause information undersupply, role ambiguity, professional and social isolation,
disengagement, job dissatisfaction and stress… UNLESS adapted well to the new conditions
Many of the changes observed in this company are adaptations to prevent such complications
To roll out effective WFH policies, employers should know how their teams adapt to WFH
26

ATTRIBUTION OF RESPONSIBILITY AND
BLAME IN A MAN-MADE DISASTER
Oz and Bisgin (2016)
Oz, Havens, and Bisgin (2018)

MOTIVATION & RESEARCH QUESTIONS
1. How common is blaming in such c.s.s.?
2. To whom it is directed at?
3. Where are the blamers from?
4. Does political predisposition affect blaming?
5. Any peer effect on blaming?
28
(APA 2020)

29
#FlintWaterCrisis
May 2014 01/16/16
665K tweets
282K people
Task Force
Final Report

(1) HOW COMMON?
63%
30
Costa Rica earthquake’12
Manila floods’13
Singapore haze’13
Queensland floods’13
Typhoon Pablo’12
Australia bushfire’13
Italy earthquakes’12
Sardinia floods’13
Philipinnes floods’12
Alberta floods’13
Typhoon Yolanda’13
Colorado floods’13
Guatemala earthquake’12
Colorado wildfires’12
Bohol earthquake’13
NY train crash’13
Boston bombings’13
LA airport shootings’13
West Texas explosion’13
Russia meteor’13
Savar building collapse’13
Lac Megantic train crash’13
Venezuela refinery’12
Glasgow helicopter crash’13
Spain train crash’13
Brazil nightclub fire’13
0
10
20
30
40
50
60
70
80
90
100
Caution & Advice
Affected Ind.
Infrast. & Utilities
Donat. & Volun.
Sympathy
Other Useful Info.
(a) Distribution of information types, sorted by descending proportion of caution and advice tweets.
(b) Distribution of information sources, sorted by descending proportion of eyewitness tweets.
Figure 1. Distributions of information types and sources (best seen in color)
Among these messages, the proportion of informative mes-
sages (i.e. those in the first category of M1) was on aver-
age 69% (min. 44%, max. 92%). Most of the messages con-
sidered “not informative” contained expressions of sympathy
and emotional support (e.g. “thoughts and prayers”).
Information types
details about the accidents and the follow-up inquiry; in
earthquakes, we find seismological details.
• Sympathy and emotional support: 20% on average
(min. 3%, max. 52%). Tweets that express sympathy are
present in all the events we examined. The 4 crises in
which the messages in this category were more prevalent
(above 40%) were all instantaneous disasters. Again, we
Collaborating Around Crisis CSCW 2015, March 14-18, 2015, Vancouver, BC, Canada
(Olteanu,Vieweg,and
Castillo2015)
Governor Snyder has been blamed
3.5 times more than the second
most blamed agent
(2) TO WHOM IT IS
DIRECTED AT?

(3) CONCERNED CITIES & COUNTIES?
Cities Counties
1 Flint, MI Genesee, MI
2 Gaylord, MI Dist Columbia, DC
3 Grand Blanc, MI Otsego, MI
4 Mount Morris, MI Wayne, MI
5 Bloomﬁeld Hills, MI Ingham, MI
6 Lansing, MI Washtenaw, MI
7 Sedona, AZ Multiple, GA
8 Davison, MI Kent, MI
9 Traverse City, MI Coconino, AZ
10 Ann Arbor, MI Cook, IL
9/10 cities are in Michigan (Top 3/4 are from Genesee)
Top 5/6 counties are from MichiganGeocoded
Normalized by population
31
H3: Flint, other cities in Genesee County, other counties in Michigan.
✓ Hypothesis supported

(4) PARTISAN PREDISPOSITION?
32
H4: Of those who blamed R (D) ideology, their sentiment toward the Governor was more (less) negative.
Flinters blaming
D ideology
Flinters blaming
R ideology
Get their tweets
mentioning Gov
Sentiments
of tweets?
Sentiments
of tweets?
Get their tweets
mentioning Gov

(5) PEER EFFECT ON SENTIMENT VALENCE?
More (–)
(N=115)
More (+)
(N=101)
Find their friends
Sentiments
of friends?
Sentiments
of friends?
Cohort Control
Get friends’ tweets
Find their friends
Get friends’ tweets
33
x̅ = -0.07
x̅ = -0.11
More (–) More (+)
(H5) Individuals with more negative (positive) expressions have friends who talk more negatively (positively)

Label the blamed
J ?
Sentiment
Analysis
Partisanship?
L ?
? L ?
Group Flinters
LLL
JJJ
Homophily?
? J ?
Find Friends
Data
#FlintWaterCrisis
01/16 – 06/16
665K tweets
282K users H4
H3
H5
Collect bio Geocode Concerned places?
Responsible
party/ideologyCONCLUSION
Research Methods
Retrospective cohort study
Sentiment analysis
Network analysis
Geocoding (GIS) analysis
Findings
Prevalence of blame discourse ✓
Partisan predisposition in blaming ✓
Concerned cities in blaming ✓
Peer/homophily effect in blaming ✓
34

DISSERTATION CONCLUSION
Research Contributions
1. Collective Stress in the Digital Age is beyond Crisis Informatics
2. Collective Stress in the Digital Age is beyond disaster response
3. Collective Stress in the Digital Age is beyond traditional research
This dissertation contributed these by producing
The first CSS study on closing the gaps between CI, CSS, Traditional CSR (Chapter 2)
The first CSS study on a recovery-phase behavior: blame (Chapter 3)
The first CSS study on measuring work-stressors (Chapter 4)
The first CSS study on heterogeneous effects of COVID-19 on work behavior (Chapter 5)
35
Social Sciences
CSS
Computational
& Data
Sciences
Collective
Stress
ResearchCrisis
Informatics
Traditional
CSR
CSS of
CSR

FUTURE WORK
New collective stressors
Algorithmic responsibility
Mis/Dis/information (trolls)
Hate crime (xenophobia)
More COVID-19 responses
More methods
Node embedding model (Deep learning)
Computational modeling (ABMs)
36

RESEARCH OUTPUTS
Full-paper peer-reviewed journal and conference publications
Oz, Talha, Crooks Andrew. 2020. “Exploring the Impact of Mandatory Remote Work during the COVID-19 Pandemic.” SBP-BRIMS
2020. https://doi.org/10.31235/osf.io/hjre6.
Oz, Talha. 2020. “Digital Trails of Work Stressors.” SBP-BRIMS 2020. https://doi.org/10.31219/osf.io/wxcqp.
Burger, Annetta, Talha Oz, William G. Kennedy, and Andrew T. Crooks. 2019. “Computational Social Science of Disasters:
Opportunities and Challenges.” Future Internet 11 (5): 103. https://doi.org/10.3390/fi11050103.
Oz, Talha, Rachael Havens, and Halil Bisgin. 2018. “Assessment of Blame and Responsibility Through Social Media in Disaster
Recovery in the Case of #FlintWaterCrisis.” Frontiers in Communication 3. https://doi.org/10.3389/fcomm.2018.00045.
Oz, Talha, and Halil Bisgin. 2016. “Attribution of Responsibility and Blame Regarding a Man-Made Disaster: #FlintWaterCrisis.”
In ArXiv:1610.03480 [Cs]. Indianapolis, IN. https://arxiv.org/abs/1610.03480.
Other related publications
Oz, Talha. 2018. “How Can Organizational Network Analysis (ONA) Help Improve Company Performance?” May 9, 2018.
https://scholar.harvard.edu/people_analytics/publications/how-can-organizational-network-analysis-ona-help-improve-company.
Burger, Annetta, Talha Oz, Andrew Crooks, and William G. Kennedy. 2017. “Generation of Realistic Mega-City Populations and
Social Networks for Agent-Based Modeling.” In Proceedings of the 2017 International Conference of The Computational Social
Science Society of the Americas, 15:1–15:7. CSS 2017. New York, NY, USA: ACM. https://doi.org/10.1145/3145574.3145593.
37

THANK YOU 🙏
Looking forward to your feedbacks
Comments?
Questions?
Suggestions?
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