![Context of Study
46 millions
people [1]
The most polluted
city worldwide[2]
Congested
City
[1] Population of National Capital Region http://goo.gl/WUSOkI
[2] World Health Organization Urban Ambient air pollution database 2016 http://goo.gl/03emdT
01](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Delhi OddEven Experiment
- 1. “OddEven” experiment through the lens of social media SofianAbbar,TaharZanouda,LaureBerti-Equille,KushalShah,Abdelkader Baggag,SanjayChawla,JaideepSrivastava Doha,10th August2016.
- 2. Context of Study 46 millions people [1] The most polluted city worldwide[2] Congested City [1] Population of National Capital Region http://goo.gl/WUSOkI [2] World Health Organization Urban Ambient air pollution database 2016 http://goo.gl/03emdT 01
- 4. “OddEven” Experiment • An initiative led by AAP leader, Arvind Kejriwal. • Allow vehicles to move on alternate days based on the registration numbers ending with odd or even digits for two weeks. 03
- 5. Research Question: What is the impact of political affiliation on people’s perception about OddEven? 04
- 6. Experiment Timeline The first phase of the 15-day pilot took place at Delhi from January 1st to January 15th 2016. Beforeexperiment After experimentDuring Experiment After the announcement, people were torn between support and opposition After the experiment, people were questioning the success of the initiative. 05 FirstWeek AfterExperiment SecondWeek AfterExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek BeforeExperiment SecondWeek BeforeExperiment
- 7. Data Data Analysis & Machine Learning techniques Findings How do we proceed? 06
- 8. Data: Types & Sources 07 Traffic Air QualityPublic OpinionData Types Google Traffic API DPCC ProgramTwitter Data Sources
- 9. Posted by users . We extracted their tweets, profiles, friends and followers to study their opinion. We collected 320,450 Tweets posted about “Odd even” urban experiment 63,988 Data: Public Opinion 08
- 10. Using Google Traffic API, we track congestion levels of main roads in Delhi. Data: Traffic Main Roads in Delhi Main Roads from outside Delhi 09
- 11. Data: Air Quality We collect data from different sources: • U.S. Embassy Air Quality Monitoring Station • DPCC Program (Delhi Pollution Control Committee) 10
- 12. Between Hope and Hype: What is the impact of Odd Even ? 11
- 13. Ground Reality Traffic did improve Public Transportation did handle demand Based on our tests, and confirmed by many studies [2][3] Based on our study, and confirmed by many studies[1] 12 [1] Business Standard: Odd-even plan: Traffic is less, pollution level isn't http://goo.gl/La4ReX [2] Indiatimes: Odd-even plan shows no strong effect on Delhi pollution http://goo.gl/LLpkJE [3] IndiaExpress: Week after odd-even, pollution levels spike in the capital, says CSE http://goo.gl/e5WV3b [4] Odd-even policy: Metro ridership sees 11 per cent increase http://goo.gl/U1cnPR [5] Odd-Even Plan: 4 Million Commuters Estimated To Have Travelled In DTC Buses http://goo.gl/sPCtoU Air Quality did not improve Based on many studies [4][5]
- 14. Can we infer people’s political leaning ?
- 15. We label users as AAP supporters, BJP supporters, apolitical users, and bi-political We use different features: • Tweets • Mentions • Friends Inference of political leaning 14
- 16. Training • Use users’ biographies to manually identify those who clearly express their political affiliation. Prediction Inference of political leaning 0 5000 10000 15000 20000 25000 30000 AAP BJP Bi-political Apolitical “I am an architect (IIT Roorkee) by profession & a rebel and activist at heart! and yes a pucca bjp supporter.“ @Chhabiy 15 Numberofusers
- 17. Volume of tweets over time Distribution of tweets volume over time Before experiment After experimentDuring Experiment The beginning of the experiment After a long weekend, supporters claimed the success of the event after 3 days People’s reaction about Arvind kejriwal speech [Watch Video]. 16
- 18. Distribution of tweets by topic Takeaways • Air Quality is the most discussed topic followed by congestion. • Public transportation topic was trending at the beginning of the experiment. 17
- 19. Users Overall sentiment distribution Takeaways • AAP followers are significantly more positive about the experiment than BJP followers. 18
- 20. Air Quality: Positivity by camp FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment AfterexperimentDuring Experiment Beforeexperiment AfterexperimentDuring Experiment Distribution of opinionated users over time Fraction of opinionated users over time AAP BJP Apolitical Bipolitical Negative Positive 19
- 21. Users • First graph captures the volume of opinionated users as well as the negativity and positivity in every camp. • Second graph captures the percentage of negativity and positivity in every camp to visually identify majority. Takeaways: • Most of users have negatively discussed Air Quality topic. • Bipolitical users were more engaged in the discussion, it might be related to the fact that these people are journalists and social activists. • AAP supporters were more optimistic before the experiment. • The number of AAP supporters who were engaged in the discussion start decreasing starting from 1st week of the experiment. 20 Air Quality: Positivity by camp
- 22. Public Transportation: Positivity by camp FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment AfterexperimentDuring ExperimentBeforeexperiment AfterexperimentDuring Experiment Distribution of opinionated users over time Fraction of opinionated users over time AAP BJP Apolitical Bipolitical Negative Positive 21
- 23. Users 22 Public Transportation: Positivity by camp • First graph captures the volume of opinionated users as well as the negativity and positivity in every camp. • Second graph captures the percentage of negativity and positivity in every camp to visually identify majority. Takeaways: • Discussion about public transportation start trending during the experiment, despite the fact that it wasn’t the main focus of the experiment. • Surprisingly, BJP supporters were more positive about the experiment during and after the experiment.
- 24. Traffic: Positivity by camp FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment AfterexperimentDuring ExperimentBeforeexperiment AfterexperimentDuring Experiment Distribution of opinionated users over time Fraction of opinionated users over time AAP BJP Apolitical Bipolitical Negative Positive 23
- 25. Users 24 Traffic: Positivity by camp • First graph captures the volume of opinionated users as well as the negativity and positivity in every camp. • Second graph captures the percentage of negativity and positivity in every camp to visually identify majority. Takeaways: • Discussion about traffic and congestion start trending during and after the experiment, as people start commenting and sharing their experiences. • AAP/BJP supporters were equally satisfied during the experiment, but majority of BJP supporters were more positive evaluating the experiment.
- 26. Users Location Air Quality (+ %/- %) Public Transportation (+ %/- %) Traffic (+ %/- %) AAP 7% / 93% 100% / 0% 100% / 0% BJP 15% / 85% 42% / 57% 86% / 14% Before Political allegiance 25 Location Air Quality (+ %/- %) Public Transportation (+ %/- %) Traffic (+ %/- %) AAP 33% / 66% 100% / 0% 33% / 66% BJP 12% / 88% 42% / 57% 21% / 78% Location Air Quality (% Pos/% Neg) Public Transportation (% Pos/% Neg) Traffic (% Pos/% Neg) AAP 13% / 87% 50% / 50% 58% / 42% BJP 15% / 85% 58% / 42% 42% / 58% Location Air Quality (% Pos/% Neg) Public Transportation (% Pos/% Neg) Traffic (% Pos/% Neg) AAP 16% / 84% 46% / 54% 53% / 47% BJP 22% / 78% 14% / 86% 47% / 53% Location Air Quality (% Pos/% Neg) Public Transportation (% Pos/% Neg) Traffic (% Pos/% Neg) AAP 17% / 83% 74% / 26% 52% / 48% BJP 5% / 95% 36% / 64% 58% / 42% Location Air Quality (% Pos/% Neg) Public Transportation (% Pos/% Neg) Traffic (% Pos/% Neg) AAP 5% / 95% 25% / 75% 8% / 92% BJP 8% / 92% 86% / 14% 47% / 43% DuringAfter First Week Second Week Location Air Quality (% Pos/% Neg) Transportation (% Pos/% Neg) Traffic (% Pos/% Neg) AAP 15% / 84% 54% / 45% 53% / 46% BJP 12% / 87% 52% / 47% 46% / 54% Overall
- 27. Air Quality: Topics, political affiliations & locations FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment AAP BJP Apolitical Bipolitical Negative Positive FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment Delhi India Outside India 26
- 28. Public Transportation: Topics, political affiliations & locations FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment AAP BJP Apolitical Bipolitical Negative Positive FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment Delhi India Outside India 27
- 29. Traffic: Topics, political affiliations & locations FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment AAP BJP Apolitical Bipolitical Negative Positive FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment FirstWeek BeforeExperiment SecondWeek BeforeExperiment FirstWeek DuringExperiment SecondWeek DuringExperiment FirstWeek AfterExperiment SecondWeek AfterExperiment Beforeexperiment After experiment DuringExperiment Delhi India Outside India 28
- 30. Users 29 • We identified place of users manually by checking their biographies, and grouped them to 4 groups {Delhi, India, WorldWide, unknown}. In this study, we are interested in 3 groups {Delhi, India, WorldWide} in this study. • The graph captures the volume of opinionated users as well as the negativity and positivity in every camp to evaluate the engagement of users, in every week. Takeaways: • BJP supporters in India engaged in discussing Traffic more than those in Delhi, due to the fact that BJP is a big national party. • Number of BJP supporters outside India who were engaged in discussion is higher than those in India. • BJP supporters outside India were more positive during the experiment than those in India. Traffic: Topics, political affiliations & locations
- 31. Users • Based on our tests, we found that Traffic has improved, but Air Quality did not improve • Based on some studies public transportation handled demand. • AAP have been more positive about the experiment compared to BJJP • People were overall satisfied with the service provided by public transportation. • BJP supporters are more ideological compared to AAP supporters. Conclusion 30
- 32. Users Questions
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