Ic2s2 Tutorial on Modeling Human Values via Social Media Data
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The document discusses modeling human values through social media, focusing on how individual moral values relate to behaviors, actions, and attitudes. It explores various motivational goals, attitudes towards climate change, charitable giving, privacy, vaccination, and the evolution of values in society. The research emphasizes the importance of understanding these values for personalized communication, policy-making, and addressing societal polarization.
![Politics
“In terms of incidence ratios, [...] the count of moral tweets on days with violent protests is 1.88 times
that of days with no protests, holding the other variables in the model constant. No such association
was observed for peaceful protest days. ”
Moralization in social networks and the
emergence of violence during protests
Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018](https://image.slidesharecdn.com/ic2s2tutorialmodelinghumanvalues1-201008134512/85/Ic2s2-Tutorial-on-Modeling-Human-Values-via-Social-Media-Data-97-320.jpg)
Ic2s2 Tutorial on Modeling Human Values via Social Media Data
- 1. Modeling Human Values with Social Media Kyriaki Kalimeri, Yelena Mejova IC2S2 - July 17, 2019 Amsterdam, The Netherlands #ValuesWorkshop
- 3. Kyriaki Kalimeri Ph.D. Brain and Cognitive Sciences @KyriakiKalimeri Yelena Mejova Ph.D. Computer Science @YelenaMejova Take photos, tweet, link! @IC2S2 #ValuesWorkshop
- 4. What is a Value?
- 8. Moral Values
- 9. Care/Harm: virtues of caring and compassion. Fairness/Cheating: unfair treatment, inequality, notions of justice. Loyalty/Betrayal: obligations of group membership, loyalty, vigilance against betrayal. Authority/Subversion: social order, obligations of hierarchical relationships such as obedience, respect Purity/Degradation: physical and spiritual contagion, virtues of chastity, wholesomeness and control of desires. Liberty/Oppression: feelings of reactance and resentment people feel toward those who dominate them and restrict their liberty
- 13. Personality & Values Values could be considered a higher level with respect to Personality Traits. Moral values determine how and when dispositions and attitudes towards interpersonal and intergroup processes relate with our life stories and narratives. Personality alone does not suffice to explain our judgments. McAdams, D., & Pals, J. (2006). A new big five: Fundamental principles for an integrative science of personality. American Psychologist , 61 , 204
- 14. Motivation A qualitative and quantitative point of view, allowing us to better model and understand behaviours, actions, and attitudes towards social phenomena.
- 15. Why modeling human values? Designing Personalised Communication Campaigns Policy-making Self-disclosure & Privacy Considerations Bridging Societal Polarization Evolution of Values Machine Ethics
- 16. Why modeling human values? Designing Personalised Communication Campaigns Policy-making Self-disclosure & Privacy Considerations Bridging Societal Polarization Evolution of Values Machine Ethics
- 18. Attitudes Towards Climate Change
- 19. CONTROL FRAMING Many people are concerned about the health of the natural environment. We are interested in what you think and feel about this issue.
- 20. INDIVIDUALISTIC FRAMING Many people around the world are concerned about the health of the natural environment. We are interested in what you think and feel about this issue. Show your love for all of humanity and the world in which we live by helping to care for our vulnerable natural environment. Help to reduce the harm done to the environment by taking action. By caring for the natural world you are helping to ensure that everyone around the world gets to enjoy fair access to a sustainable environment. Do the right thing by preventing the suffering of all life-forms and making sure that no one is denied their right to a healthy planet. SHOW YOUR COMPASSION.
- 21. SOCIAL BINDING FRAMING Many patriotic citizens of the United States are concerned about the health of the natural environment. We are interested in what you think and feel about this issue. Show you love your country by joining the fight to protect the purity of America's natural environment. Take pride in the American tradition of performing one's civic duty by taking responsibility for yourself and the land you call home. By taking a tougher stance on protecting the natural environment, you will be honoring all of Creation. Demonstrate your respect by following the examples of your religious and political leaders who defend America's natural environment. SHOW YOUR PATRIOTISM!
- 22. ● Political liberals were consistent in their pro-environmental attitudes across conditions. ● Political conservatives displayed more pro-environmental attitudes after a binding moral frame. Attitudes Towards Climate Change
- 23. Attitudes Towards Charitable Giving
- 24. Attitudes Towards Charitable Giving
- 25. Attitudes Towards Charitable Giving Perceived donation motivation is stronger for care and loyalty framing Moral Framing had NO effects neither on hypothetical not real donations.
- 26. Why modeling human values? Designing Personalised Communication Campaigns Policy-making Self-disclosure & Privacy Considerations Bridging Societal Polarization Evolution of Values Machine Ethics
- 27. Polarized attitudes towards the poor seem unlikely to be predicted by political affiliation, while moral values are more explanatory. Attitudes towards the Poor
- 28. Why modeling human values? Designing Personalised Communication Campaigns Policy-making Self-disclosure & Privacy Considerations Bridging Societal Polarization Evolution of Values Machine Ethics
- 29. Privacy, Self-Disclosure & Trust
- 30. Privacy, Self-Disclosure & Trust Individuals differ in the degree to which they desire and value personal control over information about themselves. (Berscheid, 1977) There is a negative relationship between the value people place on privacy and perceptions of control over personal information, which is the very foundation of trust (Stone, 1983)
- 31. Privacy, Self-Disclosure & Trust Higher levels of trust, larger numbers of close friends, greater amounts of social support and higher levels of civic participation.
- 32. Privacy, Self-Disclosure & Trust "But privacy is gone. We gave it up, for no other reason but the thought that it's useless. Why don't we protect our rights?"
- 33. Why modeling human values? Designing Personalised Communication Campaigns Policy-making Self-disclosure & Privacy Considerations Bridging Societal Polarization Evolution of Values Machine Ethics
- 34. Attitudes towards Vaccination Recent interventions have focused on vaccine knowledge and education and message framing as methods to change vaccination attitudes. ➔ Short-term success, but some backfire and worsen parental hesitancy. ➔ Temporarily increase vaccination rates but may lead to long-term dissatisfaction and decreased intention to vaccinate.
- 35. Attitudes towards Vaccination Endorsement of harm and fairness - ideas often emphasized in traditional vaccine-focused messages - are not predictive of vaccine hesitancy. Significant associations of purity and liberty with hesitancy.
- 36. Attitudes towards Vaccination Antivax community: trusts less the conventional authority sources (presidency, government, EU) defends more the traditional religious and moral values and believe newer lifestyles contribute to the decline of the society. Total population 1232 participants
- 37. AV pages are emphasising the value of “liberty” PV pages are emphasising the value of “family” AV Pages PV Pages Attitudes towards Vaccination
- 38. Why modeling human values? Designing Personalised Communication Campaigns Policy-making Self-disclosure & Privacy Considerations Bridging Societal Polarization Evolution of Values Machine Ethics
- 39. Are Values Changing over Time? Santos et al. Global Increases in Individualism. Psychological Science, 2017 Individualism vs. Collectivism reflects the extent to which social relationships are loose, as opposed to relationships integrated in strong and cohesive groups. Individualism increased 12% worldwide since the ‘60s. Socioeconomic development had the strongest effect, explaining between 35% and 58% of the change in individualism over time.
- 40. BREXIT
- 41. PEW RESEARCH CENTER Favoring Gay Marriage
- 42. Why modeling human values? Designing Personalised Communication Campaigns Policy-making Self-disclosure & Privacy Considerations Bridging Societal Polarization Evolution of Values Machine Ethics
- 46. Moral Artificial Intelligence 40 million decisions, in 233 countries cross-cultural ethical variation
- 48. Evolution of Surveying Methods Online & Crowdsourcing platforms (such as AMT) have been extensively validated as surveying tools and found to be as trustworthy as the traditional surveying methods.
- 51. How many hours do you spend on social networks?
- 53. Tons of Cool Studies
- 57. Evolution of Surveying Methods Online & Crowdsourcing platforms (such as AMT) have been extensively validated as surveying tools and found to be as trustworthy as the traditional surveying methods.
- 59. Social Media for Surveying Recruitment of participants is: ★ Faster ★ Scalable ★ Targeted/Customised cohort ★ Relatively limited cost ★ Efficient when monitoring fast-evolving phenomena such as crisis-response or health emergencies.
- 60. Successful Examples on Facebook Reached 4 million users & the data were used for personalization, privacy, persuasion studies Likeyouth, 60 thousand users in Italy, Employed for digital humanity studies such as youth unemployment & vaccination attitudes
- 61. The Little Prince Antoine De Saint-exupéry
- 62. Social Media for Research Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. Social data: Biases, methodological pitfalls, and ethical boundaries. 2016
- 63. Social Media as a Surveying Tool ~4,000 participants Population, Self-selection, and Behavioural biases Outcome: FB is a valid research tool for administering demographic and psychometric surveys BUT do not ignore the intrinsic platform biases. Kyriaki Kalimeri, Mariano G. Beiro, Andrea Bonanomi, Alessandro Rosina & Ciro Cattuto. Evaluation of Biases in Self-reported Demographic and Psychometric Information: Traditional versus Facebook-based Surveys
- 64. Do not ignore the intrinsic platform biases! No data source is perfect, and every dataset is imperfect in its own ways. Social Media as a Surveying Tool Zeynep Tufekci. Big questions for social media big data: Representativeness, validity and other methodological pitfalls. ICWSM, 14:505–514, 2014.
- 65. Real-time Spontaneous behaviors Geolocated Social & Individualistic Rich Multimodal Social Media Data as a Reflection of Values
- 66. Social Media Data as a Reflection of Values
- 67. Social Media Data as a Reflection of Values Real-time Geolocated Rich Multimodal Spontaneous behaviors Social & Individualistic
- 68. Social Media Data as a Reflection of Values
- 69. Social Media Data as a Reflection of Values Mobile Apps & Web Searches are greatly informative of demographics Moral & Human Values are much harder to predict, STILL, we get cool insights!
- 70. Social Media Data as a Reflection of Values Moral Values - Care Schwartz Values - Conservation - Tradition + Openness + Self-Enhancement Demographics Low income Low wealth Not a Parent Single 18 to 24 years old Not a smoker
- 71. Social Media Data as a Reflection of Values
- 72. Michael F. Schober, Josh Pasek, Lauren Guggenheim, Cliff Lampe, and Frederick G. Conrad. Social media analyses for social measurement. Public Opinion Quarterly, 80(1):180–211, 2016
- 73. Social media as reflection of values Michael F. Schober, Josh Pasek, Lauren Guggenheim, Cliff Lampe, and Frederick G. Conrad. Social media analyses for social measurement. Public Opinion Quarterly, 80(1):180–211, 2016.
- 74. Social media as reflection of values Schober et al. 2016 Initiative Informed consent Ability to opt out Prior consideration Identity of user Perceived audience Size of perceived audience Social desirability pressure Potential for manipulation Time pressure/synchrony Burden
- 75. Social media as reflection of values Schober et al. 2016 Initiative Informed consent Ability to opt out Prior consideration Identity of user Perceived audience Size of perceived audience Social desirability pressure Potential for manipulation Time pressure/synchrony Burden xkcd 1390
- 77. Social media as reflection of values Schober et al. 2016 Initiative Informed consent Ability to opt out Prior consideration Identity of user Perceived audience Size of perceived audience Social desirability pressure Potential for manipulation Time pressure/synchrony Burden Sarah Andersen
- 78. Social media as reflection of values Schober et al. 2016 Initiative Informed consent Ability to opt out Prior consideration Identity of user Perceived audience Size of perceived audience Social desirability pressure Potential for manipulation Time pressure/synchrony Burden xkcd 632
- 79. "Botornot: A system to evaluate social bots." Davis, Clayton Allen, et al. WWW, 2016. https://botometer.iuni.iu.edu Ratkiewicz et al WWW, 2011 Truthy project
- 80. "Botornot: A system to evaluate social bots." Davis, Clayton Allen, et al. WWW, 2016. Ratkiewicz et al WWW, 2011 Truthy project https://www.computerworld.com/article/2843572/computer-scientists-s ay-meme-research-doesnt-threaten-free-speech.html
- 81. Social media as reflection of values Schober et al. 2016
- 82. Social media as reflection of values Schober et al. 2016 Temporal properties Population coverage Topic coverage Sampled units Sampling frame Sampling procedure Sample size Relevance to research topic Granularity of analyses Data structure Automatically generated auxiliary information
- 83. Social media as reflection of values Schober et al. 2016 Temporal properties Population coverage Topic coverage Sampled units Sampling frame Sampling procedure Sample size Relevance to research topic Granularity of analyses Data structure Automatically generated auxiliary information xkcd 723
- 84. Can we predict which of Reddit users in Mental Health will later post in Suicide Watch?
- 85. Social media as reflection of values Schober et al. 2016 Temporal properties Population coverage Topic coverage Sampled units Sampling frame Sampling procedure Sample size Relevance to research topic Granularity of analyses Data structure Automatically generated auxiliary information https://www.smbc-comics.com/comic/2014-11-14
- 87. Social media as reflection of values Schober et al. 2016 Temporal properties Population coverage Topic coverage Sampled units Sampling frame Sampling procedure Sample size Relevance to research topic Granularity of analyses Data structure Automatically generated auxiliary information
- 88. Social media as reflection of values Schober et al. 2016
- 89. Social media as reflection of values Schober et al. 2016 Cost to researchers Research communities Ethics of consent for use of data Ethics review of research protocol Analytic approach Potential for research bias Evaluating model quality Adjustments for non-representativeness Stability of data source Ownership of data Perception of research enterprise
- 90. Social media as reflection of values Schober et al. 2016 Cost to researchers Research communities Ethics of consent for use of data Ethics review of research protocol Analytic approach Potential for research bias Evaluating model quality Adjustments for non-representativeness Stability of data source Ownership of data Perception of research enterprise xkcd 749
- 91. Social media as reflection of values Schober et al. 2016 Cost to researchers Research communities Ethics of consent for use of data Ethics review of research protocol Analytic approach Potential for research bias Evaluating model quality Adjustments for non-representativeness Stability of data source Ownership of data Perception of research enterprise xkcd 1838
- 92. Social media as reflection of values Schober et al. 2016 Cost to researchers Research communities Ethics of consent for use of data Ethics review of research protocol Analytic approach Potential for research bias Evaluating model quality Adjustments for non-representativeness Stability of data source Ownership of data Perception of research enterprise xkcd 221
- 93. Social media as reflection of values Schober et al. 2016 Cost to researchers Research communities Ethics of consent for use of data Ethics review of research protocol Analytic approach Potential for research bias Evaluating model quality Adjustments for non-representativeness Stability of data source Ownership of data Perception of research enterprise xkcd 1998
- 94. Social media as reflection of values Politics Nutrition Religion
- 95. Politics
- 96. Politics Is the moral rhetoric on Twitter related to violence during protests? 2015 Baltimore protests A moral tweet: one that is related to one of the moral foundations in Moral Foundations Theory (annotator agreement Kappa = 0.636 in moral/non-moral) Train LSTM neural network on 4,800 training tweets, achieve 89.01% accuracy Moralization in social networks and the emergence of violence during protests Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018
- 97. Politics “In terms of incidence ratios, [...] the count of moral tweets on days with violent protests is 1.88 times that of days with no protests, holding the other variables in the model constant. No such association was observed for peaceful protest days. ” Moralization in social networks and the emergence of violence during protests Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018
- 98. Politics “An independent variable is said to ‘Granger cause’ a dependent variable when previous values of the independent variable predict future values of the dependent variable above and beyond predictions based on past values of the dependent variable alone” Moralization in social networks and the emergence of violence during protests Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018
- 99. Politics “These analyses indicate a bidirectional Granger causal relationship, such that the count of moral tweets predicts the future count of arrests and the count of arrests predicts the future count of moral tweets.” Moralization in social networks and the emergence of violence during protests Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018
- 100. Politics 2 Survey studies, Example: Charlottesville, VA clashes in August 2017 Study 1: moralization vs. acceptability of using violence “To what extent protesting the far-right is a moral issue? “Violence against far-right is justifiable if fewer people join it” Confirmed, even after controlling for political orientation Study 2: moral convergence & moralization vs. acceptability of using violence Told “the majority of (versus few) people in the United States share your particular moral values” Observed “a significant interaction effect between moralization and moral convergence”, while “moral convergence was (overall) unrelated to the acceptability of violence”. Moralization in social networks and the emergence of violence during protests Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018
- 101. Politics “moralization of a protest can increase the acceptability of using violence at this protest. In addition, this only occurred when participants perceived to share their moralized attitudes with others and increased their attitude certainty.“ Moralization in social networks and the emergence of violence during protests Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018
- 102. Politics “Islamic State in Iraq and the Levant” vs “ISIS” #FailedRevolutions: Using Twitter to study the antecedents of ISIS support Magdy, Darwish, Weber. First Monday 21(2), 2016
- 103. Politics 1. full name of the group “ﻲﻣﻼﺳﻹا ”ﺔﻟودﻟا (Aldawla Alislamiya — “Islamic State”) or “ﻲﻓ قارﻌﻟا مﺎﺷﻟاو ”ﺔﯾﻣﻼﺳﻹا (Aldawla Alislamiya fi Aliraq walsham — “Islamic State in Iraq and the Levant”) 2. abbreviated version of the name “”عاد (da’esh — Arabic acronym for the group), “”شﻋ (da’eshy — from da’esh), or “”عاود (dawa’esh — plural of da’eshy) 3.9 million tweets 13 Oct - 31 Dec 2014 #FailedRevolutions: Using Twitter to study the antecedents of ISIS support Magdy, Darwish, Weber. First Monday 21(2), 2016
- 104. Politics #FailedRevolutions: Using Twitter to study the antecedents of ISIS support Magdy, Darwish, Weber. First Monday 21(2), 2016 1. full name of the group “ﻲﻣﻼﺳﻹا ”ﺔﻟودﻟا (Aldawla Alislamiya — “Islamic State”) or “ﻲﻓ قارﻌﻟا مﺎﺷﻟاو ”ﺔﯾﻣﻼﺳﻹا (Aldawla Alislamiya fi Aliraq walsham — “Islamic State in Iraq and the Levant”) 2. abbreviated version of the name “”عاد (da’esh — Arabic acronym for the group), “”شﻋ (da’eshy — from da’esh), or “”عاود (dawa’esh — plural of da’eshy) 3.9 million tweets 13 Oct - 31 Dec 2014 Select accounts mentioning ISIS at least 10 times And which use same name form 70% of the time Manually assessed accuracy 98%
- 105. Politics #FailedRevolutions: Using Twitter to study the antecedents of ISIS support Magdy, Darwish, Weber. First Monday 21(2), 2016 Hashtags predicting ISIS support
- 106. Politics #FailedRevolutions: Using Twitter to study the antecedents of ISIS support Magdy, Darwish, Weber. First Monday 21(2), 2016 Hashtags predicting ISIS opposition
- 107. Politics #FailedRevolutions: Using Twitter to study the antecedents of ISIS support Magdy, Darwish, Weber. First Monday 21(2), 2016
- 108. Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017
- 109. Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017 Most important factors for choosing a president are: ● Party / partisanship ● Policy considerations ● Personality of candidate
- 110. Nov 15, 2015 - Feb 29, 2016 49,564,856 tweets 11,043,452 users Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017 Twitter Streaming API Candidate full name as query
- 111. Party User following “landmark” accounts with known partisanship Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017 Personality Modified Adjective Check List (ACL) having 14 categories: moderation, forcefulness, pettiness... Policy From previous literature, keywords for 11 policy categories: immigration, economy, gun control... Sentiment SentiWordNet which is expansion of WordNet dictionary
- 112. “negative tweets account for about four-fifths of all personality-related tweets overall, Republicans tend to be more negative than Democrats (83% vs. 77%). As compared to Republicans, Democrats are more positive about pacifism and more negative about machiavellianism and wit.” Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017
- 113. Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017 Clinton is seen as moderate, but not Trump or Sanders Sanders is seen as most friendly and cute Clinton and Christie are seen as most machiavellian
- 114. Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017
- 115. Are these important in modeling favorability ratings of politicians? Linear regression on polls aggregated by RealClearPolitics Feature selection using AIC, modeling Republicans and Democrats separately Politics Revisiting the American Voter on Twitter Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017
- 116. Politics Track affiliation with an idea Track changes over time Qualitative data to understand values Social interaction
- 117. Nutrition
- 118. Nutrition ● 164K locations ● 20.8M posts ● 3.3M users ● 316 US counties #FoodPorn: Obesity Patterns in Culinary Interactions Mejova, Haddadi, Noulas, Weber. DH, 2015
- 119. Nutrition ● Users post more photos in non-fast-food restaurants ● Correlation between obesity rate & fast food check-ins is 0.424 #FoodPorn: Obesity Patterns in Culinary Interactions Mejova, Haddadi, Noulas, Weber. DH, 2015
- 120. Nutrition ● Posts from low-obesity counties get more likes and comments than in high-obesity areas (socio-economic correlates?) ● More information can be glimpsed from other hashtags associated with visiting restaurants #FoodPorn: Obesity Patterns in Culinary Interactions Mejova, Haddadi, Noulas, Weber. DH, 2015
- 121. Nutrition #FoodPorn: Obesity Patterns in Culinary Interactions Mejova, Haddadi, Noulas, Weber. DH, 2015 Restaurant categories with #foodporn Likes on posts from restaurant categories
- 122. Nutrition Anorexia on Tumblr: A Characterization Study De Choudhury. DH, 2015 WARNING: DISTURBING BODY IMAGERY
- 123. Values are inferred from posted and liked content Surveying these vulnerable populations directly may be difficult Nutrition Anorexia on Tumblr: A Characterization Study De Choudhury. DH, 2015 Predicting pro-ana vs pro-recovery users
- 124. fitspiration, fitsporation, fitspo n = 1,050 posts March 25-26, 2015 Nutrition Skinny is not enough: A content analysis of fitspiration on Pinterest Simpson & Mazzeo. Health Comms, 2017
- 125. Nutrition Skinny is not enough: A content analysis of fitspiration on Pinterest Simpson & Mazzeo. Health Comms, 2017 Appearance vs Health Focus Body Image Standards Outcome Expectations
- 126. Nutrition 1,055,196 tweets: obese or obesity 2,889,764 tweets: diabetes or diabetic July 19, 2017 - Dec 31, 2017 Information Sources and Needs in the Obesity and Diabetes Twitter Discourse Mejova. DH, 2018
- 127. Nutrition Information Sources and Needs in the Obesity and Diabetes Twitter Discourse Mejova. DH, 2018 Hand-annotated topics of questions Prevalent discussion of whether obesity is a disease and whether social acceptance of obesity as disease is desirable Diabetes has smaller discussion on social perception of diabetics
- 128. Nutrition Information Sources and Needs in the Obesity and Diabetes Twitter Discourse Mejova. DH, 2018 Crowdsourced annotation Fat shaming: 27.6% Obesity, 5.9% Diabetes Personal information: 16.3% Obesity, 24.4% Diabetes Personal responsibility: 9.4% Obesity, 8.2% Diabetes
- 129. Nutrition Values revealed via observed behavior Values may be unhealthy, merging into mental problems Values may be promoted by people or other parties Discussions reveal attitudes
- 130. Religion
- 131. Religion Halal ﺣﻼﻻ In Islam, Halal is an Arabic term meaning “lawful or permissible” and not only encompasses food and drink, but all matters of daily life. Haram That which is not permissible under Islamic law #Halal Culture on Instagram Mejova, Benkhedda, Khairani. Frontiers in Digital Humanities 2017 Source: Islamic Services of America
- 132. Halal (English) 1,004,445 posts by 120,943 users ﺣﻼﻻ (Arabic) 325,665 posts by 11,516 users Data collection in April 2016 Religion #Halal Culture on Instagram Mejova, Benkhedda, Khairani. Frontiers in Digital Humanities 2017
- 133. Religion #Halal Culture on Instagram Mejova, Benkhedda, Khairani. Frontiers in Digital Humanities 2017 English ArabicIndonesian Topic extraction using LDA, the 50 topics annotated using Grounded Theory with primary and secondary codes by proficient language speakers.
- 134. Posts mentioning religion get many more likes in Arabic (but everything else does too) Posts mentioning foods get fewer likes when they also mention regulatory agencies like FDA (in Indonesian and English) Religion #Halal Culture on Instagram Mejova, Benkhedda, Khairani. Frontiers in Digital Humanities 2017 EnglishArabic Indonesian
- 135. Social media may be a window into the open interpretation and changing nature of religious concepts Religion #Halal Culture on Instagram Mejova, Benkhedda, Khairani. Frontiers in Digital Humanities 2017
- 136. Religion Tweet histories of over 12K users who claim to live in Qatar and have a common Qatari last name. Span Sep 2006 - July 2014 Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017
- 137. ● No single word for “privacy” ● Manual analysis of popular keywords, excluding ambiguous ones ● LDA topical analysis to exclude non-relevant topics ● Final tweets: 1,772 Religion Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017
- 138. Mainly associated with fear, demerit, haram, culture, society, people. Also: Facebook and policy Spaces in which privacy should apply Religion Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017 Terms associated with Khososyah (privacy)
- 139. Anything that should not be disclosed in public. Close connection to God “Do not violate others’ privacy if you do not want your own privacy violated” Accumulation of good deeds Religion Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017 Terms associated with Ird (honor)
- 140. Computer Mediated Discourse Analysis Religion Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017
- 141. ● manually labeled for gender ● user’s name, screen name, profile ● 47% female ● contributed 1.367 tweets (men 1.445 tweets) Religion Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017 more female more male
- 142. Privacy is gendered Privacy is what one is exposed to Privacy is also after death Privacy is moral in religious sense Religion Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017
- 143. pro-Muslim vs anti-Muslim response after November 13, 2015 Paris attacks 8.36M tweets on Nov 14 - 16 Religion #isisisnotislam or #deportallmuslims?: Predicting unspoken views Magdy, Darwish, Abokhodair, Rahimi, Baldwin. WebSci, 2016
- 144. Predicting post-even stance using content, profile, and network features and SVM classifier Religion #isisisnotislam or #deportallmuslims?: Predicting unspoken views Magdy, Darwish, Abokhodair, Rahimi, Baldwin. WebSci, 2016
- 145. Negative Positive
- 146. Conservative media outlets Presidential primaries Evangelical Christian preachers Political and foreign issues pro-Israel media Atheists Secular Muslims anti-Islam content Abortion Liberal media outlets Presidential primaries US President (Obama) Social issues like abortion, race relations, same sex marriage, gun control Foreign media outlets Muslim academics Support for Muslims African American media Negative Positive
- 147. Religion Track the way religion affects everyday behaviors & beliefs Track opinions about religion Location-specific culture Development over time
- 148. Does Social Media change our values?
- 149. Discussion
- 150. Discussion Data ownership Data / model bias Informed / meaningful consent Ethical intervention design
- 151. Discussion - data ownership
- 152. Discussion - bias Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. Social data: Biases, methodological pitfalls, and ethical boundaries. 2016.
- 153. Discussion - bias Zeynep Tufekci. Big questions for social media big data: Representativeness, validity and other methodological pitfalls. ICWSM, 14:505–514, 2014. Twitter becoming a model organism because of accessible API and public data. But is it representative of interactions on internet?
- 154. Discussion - meaningful consent ● Disclosure: provide participant with accurate information about benefits and harms ● Comprehension: participant must understand what is being disclosed ● Voluntariness: participant can reasonably resist participation ● Competence: participant has mental, emotional and physical competences to give informed consent ● Agreement: participant must have clear opportunity to accept or decline ● Minimal Distraction: participant’s attention should not be diverted Friedman, B., Lin, P., & Miller, J. K. (2005). Informed consent by design. Security and Usability, 503-530.
- 155. Discussion - ethical interventions Who defines the desirable values?
- 157. Data http://www.worldvaluessurvey.org/WVSContents.jsp Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.F. and Rahwan, I., 2018. The moral machine experiment. Nature, 563(7729), p.59. https://likeyouth.org/ https://osf.io/k5n7y/ https://github.com/oaraque/moral-foundations
- 158. Shalom H Schwartz. 2012. An overview of the Schwartz theory of basic values.Online Readings in Psychology and Culture 2, 1 (2012), 11. Haidt, J., 2003. The moral emotions. Handbook of affective sciences, 11(2003), pp.852-870. Jonathan Haidt and Craig Joseph. 2004. Intuitive ethics: How innately prepared intuitions generate culturally variable virtues. Daedalus 133, 4 (2004), 55–66. Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S.P. and Ditto, P.H., 2013. Moral foundations theory: The pragmatic validity of moral pluralism. In Advances in experimental social psychology (Vol. 47, pp. 55-130). Academic Press. Iyer, R., Koleva, S., Graham, J., Ditto, P. and Haidt, J., 2012. Understanding libertarian morality: The psychological dispositions of self-identified libertarians. PloS one, 7(8), p.e42366. Feldman, G. Personal values and moral foundations: Examining relations and joint prediction of moral variables. McAdams, D., & Pals, J. (2006). A new big five: Fundamental principles for an integrative science of personality. American Psychologist , 61 , 204 Wolsko, C., Ariceaga, H., & Seiden, J. (2016). Red, white, and blue enough to be green: Effects of moral framing on climate change attitudes and conservation behaviors. Journal of Experimental Social Psychology, 65 , 7 - 19. http://www.sciencedirect.com/science/article/pii/S0022103116301056. doi:http://dx.doi.org/10.1016/j.jesp.2016.02.005. Citations
- 159. Low, M., & Wui, M. G. L. (2015). Moral foundations and attitudes towards the poor. Current Psychology, (pp. 1 - 11). URL: http://dx.doi.org/10.1007/s12144-015-9333-y. doi:10.1007/s12144-015-9333-y. Amin, A. B., Bednarczyk, R. A., Ray, C. E., Melchiori, K. J., Graham, J., Huntsinger, J. R., & Omer, S. B. (2017). Association of moral values with vaccine hesitancy. Nature Human Behaviour , 1 , 873 - 880. doi:10.1038/s41562-017-0256-5. Kalimeri, K., Beiró, M.G., Urbinati, A., Bonanomi, A., Rosina, A., & Cattuto, C. (2019). Human Values and Attitudes towards Vaccination in Social Media. WWW. Hoover, J., Johnson, K., Boghrati, R., Graham, J., & Dehghani, M. (2018). Moral framing and charitable donation: Integrating exploratory social media analyses and confirmatory experimentation. Collabra: Psychology, 4(1). Santos et al. Global Increases in Individualism. Psychological Science, 2017 Miriam J. Metzger, Privacy, Trust, and Disclosure: Exploring Barriers to Electronic Commerce, Journal of Computer-Mediated Communication, Volume 9, Issue 4, 1 July 2004, JCMC942, https://doi.org/10.1111/j.1083-6101.2004.tb00292.x Heath, R. L. & Bryant, J. (1992). Human communication theory and research . Hillsdale, NJ: Lawrence Erlbaum. Roloff, M. E. (1981). Interpersonal communication: The social exchange approach . Beverly Hills, CA: Sage. Snyder, M., Tanke, E. D., & Berscheid, E. (1977). Social perception and interpersonal behavior: On the self-fulfilling nature of social stereotypes. Journal of Personality and Social Psychology, 35(9), 656-666. Stone, E. F., Gueutal, H. G., Gardner, D. G., & McClure, S. (1983). A field experiment comparing information-privacy values, beliefs, and attitudes across several types of organizations. Journal of Applied Psychology , 68(3), 459–468. Nyhan, B., Reifler, J., Richey, S. & Freed, G. L. Effective messages in vaccine promotion: a randomized trial. Pediatrics 133, E835–E842 (2014).
- 160. Opel, D. J. et al. The influence of provider communication behaviors on parental vaccine acceptance and visit experience. Am. J. Public Health 105, 1998–2004 (2015). Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014. World Values Survey: Round Six - Country-Pooled Datafile Version: http://www.worldvaluessurvey.org/WVSDocumentationWV6.jsp. Madrid: JD Systems Institute. Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.F. and Rahwan, I., 2018. The moral machine experiment. Nature, 563(7729), p.59. Melissa A Baker, Paul Fox, and JD Twila Wingrove. Crowdsourcing as a forensic psychology research tool. American Journal of Forensic Psychology, 34(1):37, 2016. Tara S Behrend, David J Sharek, Adam W Meade, and Eric N Wiebe. The viability of crowdsourcing for survey research. Behavior research methods, 43(3):800, 2011. Michael Buhrmester, Tracy Kwang, and Samuel D Gosling. Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data? Perspectives on psychological science, 6(1):3–5, 2011. Matthew J C Crump, John V Mcdonnell, and Todd M Gureckis. Evaluating Amazon’s Mechanical Turk as a Tool for Experimental Behavioral Research. PloS one, 8(3), 2013. Laura Germine, Ken Nakayama, Bradley C. Duchaine, Christopher F. Chabris, Garga Chatterjee, and Jeremy B. Wilmer. Is the web as good as the lab? comparable performance from web and lab in cognitive/perceptual experiments. Psychonomic Bulletin & Review, 19(5):847–857, 2012. Samuel D Gosling and Winter Mason. Internet research in psychology. Psychology, 66,2015. Edith Law, Andrea Wiggins, Mary L Gray, and Alex Williams. Crowdsourcing as a Tool for Research : Implications of Uncertainty. In Proceedings of the 20th ACM Conference on Computer Supported Cooperative Work and Social Computing (To appear). ACM, 2017.
- 161. Winter Mason and Siddharth Suri. Conducting behavioral research on Amazon’s Mechanical Turk. Behavior research methods, 44(1):1–23, 2012. Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129-152. Macy, M. W. (2015). An Emerging Trend: Is Big Data the End of Theory?. Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource, 1-14. Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016). Social data: Biases, methodological pitfalls, and ethical boundaries. Methodological Pitfalls, and Ethical Boundaries (December 20, 2016). Kyriaki Kalimeri, Mariano G. Beiro, Andrea Bonanomi, Alessandro Rosina & Ciro Cattuto. Evaluation of Biases in Self-reported Demographic and Psychometric Information: Traditional versus Facebook-based Surveys arXiv URL: https://arxiv.org/abs/1901.07876 Zeynep Tufekci. Big questions for social media big data: Representativeness, validity and other methodological pitfalls. ICWSM, 14:505–514, 2014. Hoover, J., Portillo-Wightman, G., Yeh, L., Havaldar, S., Davani, A. M., Lin, Y., … Dehghani, M. (2019, April 10). Moral Foundations Twitter Corpus: A collection of 35k tweets annotated for moral sentiment. https://doi.org/10.31234/osf.io/w4f72 Boyd, R. L., Wilson, S. R., Pennebaker, J. W., Kosinski, M., Stillwell, D. J., & Mihalcea, R. (2015, April). Values in words: Using language to evaluate and understand personal values. In Ninth International AAAI Conference on Web and Social Media. Novak, P. K., Smailović, J., Sluban, B., & Mozetič, I. (2015). Sentiment of emojis. PloS one, 10(12), e0144296. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. Araque, O., Gatti, L., & Kalimeri, K. (2019). MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction. arXiv preprint arXiv:1904.08314.
- 162. Chen, J., Hsieh, G., Mahmud, J. U., & Nichols, J. (2014, February). Understanding individuals' personal values from social media word use. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 405-414). ACM. Kalimeri, K., Beiró, M. G., Delfino, M., Raleigh, R., & Cattuto, C. (2019). Predicting demographics, moral foundations, and human values from digital behaviours. Computers in Human Behavior, 92, 428-445. Michael F. Schober, Josh Pasek, Lauren Guggenheim, Cliff Lampe, and Frederick G. Conrad. Social media analyses for social measurement. Public Opinion Quarterly, 80(1):180–211, 2016 Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790. Ratkiewicz, Jacob, et al. "Truthy: mapping the spread of astroturf in microblog streams." Proceedings of the 20th international conference companion on World wide web. ACM, 2011. Davis, Clayton Allen, et al. "Botornot: A system to evaluate social bots." Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, 2016. De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., & Kumar, M. (2016, May). Discovering shifts to suicidal ideation from mental health content in social media. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 2098-2110). ACM. Stephens-Davidowitz, Seth, and Andrés Pabon. Everybody lies: Big data, new data, and what the internet can tell us about who we really are. New York, NY: HarperCollins, 2017. Mooijman, M., Hoover, J., Lin, Y., Ji, H., & Dehghani, M. (2018). Moralization in social networks and the emergence of violence during protests. Nature Human Behaviour, (p. 1). Magdy, Walid, Kareem Darwish, and Ingmar Weber. "#FailedRevolutions: Using Twitter to study the antecedents of ISIS support." First Monday 21(2) (2016).
- 163. Angus Campbell, Converse Philip, Miller Warren, and Stokes Donald. 1960. The American Voter. University of Chicago Press. Le, H. T., Boynton, G. R., Mejova, Y., Shafiq, Z., & Srinivasan, P. (2017, May). Revisiting the american voter on twitter. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 4507-4519). ACM. Mejova, Y., Haddadi, H., Noulas, A., & Weber, I. #Foodporn: Obesity patterns in culinary interactions. In Proceedings of the 5th International Conference on Digital Health 2015 (pp. 51-58). ACM. Simpson, Courtney C., and Suzanne E. Mazzeo. "Skinny is not enough: A content analysis of fitspiration on Pinterest." Health communication 32, no. 5 (2017): 560-567. Yelena Mejova. Information Sources and Needs in the Obesity and Diabetes Twitter Discourse. ACM International Conference on Digital Health (DH), 2018. Yelena Mejova, Youcef Benkhedda, Khairani. #Halal Culture on Instagram. Frontiers in Digital Humanities: Big Data, 02. 2017. Norah Abokhodair, Sofiane Abbar, Sarah Vieweg, Yelena Mejova. Privacy and Social Media Use in the Arabian Gulf: Saudi Arabian & Qatari Traditional Values in the Digital World. The Journal of Web Science 3 (1), 2017. Magdy, Walid, Kareem Darwish, Norah Abokhodair, Afshin Rahimi, and Timothy Baldwin. "# isisisnotislam or# deportallmuslims?: Predicting unspoken views." In Proceedings of the 8th ACM Conference on Web Science, pp. 95-106. ACM, 2016. Swigger, N. (2013). The online citizen: Is social media changing citizens’ beliefs about democratic values? Political Behavior, 35(3), 589–603. Kurpad, Sunita Simon. "Ethics in psychosocial interventions." Indian journal of psychiatry 60.Suppl 4 (2018): S571.