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COLLECTIVE STRESS IN
THE DIGITAL AGE
Talha Oz
Computational Social Science
PhD Defense - 10/27/2020
Advisor: Andrew T. Cro...
OUTLINE
Introduction
Studies
1. Measuring Work Stressors
2. Impact of COVID-19 on Work
3. Attribution of Blame on #FlintWa...
3
STRESS PROCESS
Stressors: life events, disasters,
discrimination, oppressions, etc.
Social contexts: socioeconomic
statu...
4
Macro
Scope/SocialContext
Micro
Chronic /
Continuous
The Stress ContinuumSudden /
Discrete
Systemic racism in a
country
...
STRESS IS COMMON, COSTLY, & TRENDING
50
55
60
65
70
75
80
85
2017 2018 2019 2020
Percentage
% of Americans reporting that ...
RESEARCH MOTIVATION
ARjats.cls April 18, 2020 9:14
Business
Psychology
Education
Political
science
Sociology
0
50
100
150
...
RESEARCH QUESTIONS
• How to model work stressors with digital exhaust & make use of the models effectively?Study-1
• How d...
DIGITAL TRAILS OF WORK STRESSORS Oz (2020)
Intro Study-1 Study-2 Study-3 Conclusion
The health effects of work stressors vs secondhand smoking exposure (Goh et al. 2015)
BACKGROUND
9
Counseling
Services
Res...
STATE OF THE ART
Measurement Methods
­ Surveys
­ (+) Depth & breadth
­ (–) Biases (esp. for obj. stressors)
­ (–) Costly (...
RESEARCH QUESTIONS
Digital trails/exhaust = Metadata (no content)
­ Email: MS-Exchange / G-workspace
­ Scheduled meetings:...
EXAMPLE: MODELING A STRESSOR INDICATOR
Let’s go over an example: Work-life Balance (WLB)
1. How to model it? Two measurabl...
STRUCTURAL / NETWORK STRESSORS
Network construction
­ Combine (sum) all streams (over a time period)
­ unscheduled calls +...
TIME MANAGEMENT STRESSORS
Meetings not start/end on time (calendar)
­ People join/leave times (room sensors; Zoom)
Beyond ...
RQ2: ONCE MODELED, YOU CAN…
­ Check against acceptable range: safe/critical
­ Slice & dice (by team, level, job function, ...
Proposed strategy allows
­ Objective measurement
­ better than state-of-the-art (SOTA)
­ Unobtrusive
­ No cost to employee...
THE IMPACT OF COVID-19 ON INTRA-FIRM
COMMUNICATION: BAU VS WFH
Oz and Crooks (2020)
Intro Study-1 Study-2 Study-3 Conclusi...
BACKGROUND
COVID-19 (March 2020)
­ +34% of US employees started to WFH
Employers ask
­ What WFH policies do I need: can da...
DATA & METHOD
A Tech Company
­ N=35 (ET-office = 26, PT-office = 9)
­ Five months: 2020-01-06 to 2020-05-31
­ Pre (BAU): 0...
HYPOTHESES & FINDINGS
(H1a) CMC increases within-office but not much across offices
(H1b) and this increase is driven by w...
HYPOTHESES & FINDINGS
(H2) Much of the within-team f2f move to Messaging rather than Meeting (✓)
­ Maintains connection (#...
HYPOTHESES & FINDINGS
(H3) Employees setup extra (short) meetings and (H4) remove long meetings (✓, ✓)
­ Switch media: com...
HYPOTHESES & FINDINGS
23
(H5) In WFH, employees communicate outside regular hours more (✓)
­ Flexibility & autonomy prevai...
HYPOTHESES & FINDINGS
24
(H7) Cross-level communication increases more than that of same-level (✓)
­ More supervisor suppo...
HYPOTHESES & FINDINGS
(H8) Message turnaround would be shorter in WFH (✓)
­ Greater level of telepressure when WFH
(H9) Re...
CONCLUSION
Findings supported the hypotheses based on the theories of
­ Computer mediated communication (Nardi and Whittak...
ATTRIBUTION OF RESPONSIBILITY AND
BLAME IN A MAN-MADE DISASTER
Oz and Bisgin (2016)
Oz, Havens, and Bisgin (2018)
Intro St...
MOTIVATION & RESEARCH QUESTIONS
1. How common is blaming in such c.s.s.?
2. To whom it is directed at?
3. Where are the bl...
29
#FlintWaterCrisis
May 2014 01/16/16
665K tweets
282K people
Task Force
Final Report
Intro Study-1 Study-2 Study-3 Concl...
(1) HOW COMMON?
63%
30
Costa Rica earthquake’12
Manila floods’13
Singapore haze’13
Queensland floods’13
Typhoon Pablo’12
Aus...
(3) CONCERNED CITIES & COUNTIES?
Cities Counties
1 Flint, MI Genesee, MI
2 Gaylord, MI Dist Columbia, DC
3 Grand Blanc, MI...
(4) PARTISAN PREDISPOSITION?
32
H4: Of those who blamed R (D) ideology, their sentiment toward the Governor was more (less...
(5) PEER EFFECT ON SENTIMENT VALENCE?
More (–)
(N=115)
More (+)
(N=101)
Find their friends
Sentiments
of friends?
Sentimen...
Label the blamed
J ?
Sentiment
Analysis
Partisanship?
L ?
? L ?
Group Flinters
LLL
JJJ
Homophily?
? J ?
Find Friends
Data
...
DISSERTATION CONCLUSION
Research Contributions
1. Collective Stress in the Digital Age is beyond Crisis Informatics
2. Col...
FUTURE WORK
­ New collective stressors
­ Algorithmic responsibility
­ Mis/Dis/information (trolls)
­ Hate crime (xenophobi...
RESEARCH OUTPUTS
Full-paper peer-reviewed journal and conference publications
­ Oz, Talha, Crooks Andrew. 2020. “Exploring...
THANK YOU 🙏
Looking forward to your feedbacks
­ Comments?
­ Questions?
­ Suggestions?
38
Intro Study-1 Study-2 Study-3 Con...
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Collective Stress in the Digital Age

  1. 1. 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
  2. 2. 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. 3. 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 Intro Study-1 Study-2 Study-3 Conclusion
  4. 4. 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 Intro Study-1 Study-2 Study-3 Conclusion
  5. 5. 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 Intro Study-1 Study-2 Study-3 Conclusion 2008: +46K lives lost
  6. 6. 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 Intro Study-1 Study-2 Study-3 Conclusion
  7. 7. 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) Intro Study-1 Study-2 Study-3 Conclusion
  8. 8. DIGITAL TRAILS OF WORK STRESSORS Oz (2020) Intro Study-1 Study-2 Study-3 Conclusion
  9. 9. 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 Intro Study-1 Study-2 Study-3 Conclusion
  10. 10. 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 Intro Study-1 Study-2 Study-3 Conclusion Objective reality or just a shared perception?
  11. 11. 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? Intro Study-1 Study-2 Study-3 Conclusion
  12. 12. 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. Intro Study-1 Study-2 Study-3 Conclusion
  13. 13. 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 Intro Study-1 Study-2 Study-3 Conclusion
  14. 14. 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 Intro Study-1 Study-2 Study-3 Conclusion
  15. 15. 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) Intro Study-1 Study-2 Study-3 Conclusion
  16. 16. 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 Intro Study-1 Study-2 Study-3 Conclusion
  17. 17. THE IMPACT OF COVID-19 ON INTRA-FIRM COMMUNICATION: BAU VS WFH Oz and Crooks (2020) Intro Study-1 Study-2 Study-3 Conclusion
  18. 18. 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 Intro Study-1 Study-2 Study-3 Conclusion
  19. 19. 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: !"#$%&#!' %!""&(! )*$#$+( #$%! # -. *!/!$0!*" • Meetings: %!!#$+( #$%! # -. 1&*#$/$1&+#" 2 3 • Aggregate pairs weekly • (week1, p1, p2, messaging _am) • (week1, p1, p2, meeting_am) Intro Study-1 Study-2 Study-3 Conclusion
  20. 20. HYPOTHESES & FINDINGS (H1a) CMC increases within-office but not much across offices (H1b) and this increase is driven by within-team communication 20 Intro Study-1 Study-2 Study-3 Conclusion PT Office: A, B ET Office: C, D, EPre/BAU: Post/WFH: A B C D E (1 team) (3 teams)
  21. 21. HYPOTHESES & FINDINGS (H2) Much of the within-team f2f move to Messaging rather than Meeting (✓) ­ Maintains connection (#channels, chat history) ­ Context-rich (emojis) ­ Coordination (MPDMs, apps, bots) ­ Quick questions/clarifications on IM is faster than dialing in ­ Negotiates availability (convenience asymmetry) –even better than shared space 21 Within-team comm. BAU (min) WFH (min) Change (min) Change ratio Meeting 212.41 219.19 +6.78 +3.19 % Messaging 197.57 245.78 +48.21 +24.40 % Intro Study-1 Study-2 Study-3 Conclusion 87.7% = %!""&($+( $+/*!&"! #-#&4 $+/*!&"!
  22. 22. HYPOTHESES & FINDINGS (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 % Intro Study-1 Study-2 Study-3 Conclusion
  23. 23. HYPOTHESES & FINDINGS 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). Intro Study-1 Study-2 Study-3 Conclusion
  24. 24. HYPOTHESES & FINDINGS 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 % Intro Study-1 Study-2 Study-3 Conclusion
  25. 25. HYPOTHESES & FINDINGS (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 Intro Study-1 Study-2 Study-3 Conclusion
  26. 26. 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 Intro Study-1 Study-2 Study-3 Conclusion
  27. 27. ATTRIBUTION OF RESPONSIBILITY AND BLAME IN A MAN-MADE DISASTER Oz and Bisgin (2016) Oz, Havens, and Bisgin (2018) Intro Study-1 Study-2 Study-3 Conclusion
  28. 28. 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 Intro Study-1 Study-2 Study-3 Conclusion (APA 2020)
  29. 29. 29 #FlintWaterCrisis May 2014 01/16/16 665K tweets 282K people Task Force Final Report Intro Study-1 Study-2 Study-3 Conclusion
  30. 30. (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) Intro Study-1 Study-2 Study-3 Conclusion Governor Snyder has been blamed 3.5 times more than the second most blamed agent (2) TO WHOM IT IS DIRECTED AT?
  31. 31. (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 Intro Study-1 Study-2 Study-3 Conclusion
  32. 32. (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 ✓ Hypothesis supported Intro Study-1 Study-2 Study-3 Conclusion
  33. 33. (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) ✓ Hypothesis supported Intro Study-1 Study-2 Study-3 Conclusion
  34. 34. 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 Intro Study-1 Study-2 Study-3 Conclusion
  35. 35. 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 Intro Study-1 Study-2 Study-3 Conclusion Social Sciences CSS Computational & Data Sciences Collective Stress ResearchCrisis Informatics Traditional CSR CSS of CSR
  36. 36. 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 Intro Study-1 Study-2 Study-3 Conclusion
  37. 37. 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 Intro Study-1 Study-2 Study-3 Conclusion
  38. 38. THANK YOU 🙏 Looking forward to your feedbacks ­ Comments? ­ Questions? ­ Suggestions? 38 Intro Study-1 Study-2 Study-3 Conclusion

Talha Oz's Computational Social Science PhD Defense Presentation at George Mason University

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