Social Media and Policing: Computational Approaches to Enhancing Collaborative Action between Residents and Law Enforcement

1. SOCIAL MEDIA & POLICING: Computational Approaches to Enhancing Collaborative Action between Residents and Law Enforcement Niharika Sachdeva PhD Thesis Defense TCS Research Scholar niharikas@iiitd.ac.in

2. Who am I? – Ph.D. student – Senior Research Scientist @Philips Research, India – TCS Research Scholar – Done work in computer mediated communication and usable security (HCI) – Research interests – Collaboration and communication – Machine Learning – Human computer interaction – Usable security and privacy 2

3. India is Biggest Police Department 3 238 Police Officers per 100,000 129 Police Officers per 100,000 327 Police Officers per 100,000 Which is India? South Africa? USA?

4. 4 Most Overworked – Need Help!

5. Collaborating with Residents One – way communication 5 Two – way communication Asynchronous, Remote and Public platform for Interaction Need for Improved Collective Action and Accountability

6. How about Interacting with Police on OSM? 6 – How many of you are on Facebook / Twitter? – How many of you know about social media police pages / accounts or use them to interact with police?

7. New Media to Stay Connected 7

8. Three Dimensions for Successful Collaboration High More accurate analytical and modeling tools Low High High More people involved More data available Police Resident Charalabidis, Yannis, and Sotirios Koussouris, eds. Empowering open and collaborative governance: Technologies and methods for online citizen engagement in public policy making. Springer Science & Business Media, 2012. 8

9. Challenges: Successful Collaboration – Identifying how social media can support day-to-day interaction between police and residents – Analyzing and Extracting meaningful and actionable information from enormous data – Unstructured and unconstrained – Inferring actionable information – Quantifying behavior (emotions and linguistic attributes) – Maintaining responsiveness to residents – Promptness and timely action by police on social media – Engaging with people 9 High More accurate analytical and modeling methods Low High High More people involved More data available

10. Challenges: Successful Collaboration – Identifying how social media can support day-to- day interaction between police and residents – Analyzing and Extracting meaningful and actionable information from enormous data – Unstructured and unconstrained – Inferring actionable information – Quantifying behavior (emotions and linguistic attributes) – Maintaining responsiveness to residents – Promptness and timely action by police on social media – Engaging with people 10 High Low High More people involved More data available More accurate analytical and modeling methods High

11. Challenges: Successful Collaboration – Identifying how social media can support day-to- day interaction between police and residents – Analyzing and Extracting meaningful and actionable information from enormous data – Unstructured and unconstrained – Inferring actionable information – Quantifying behavior (emotions and linguistic attributes) – Maintaining responsiveness to residents – Promptness and timely action by police on social media – Keep engaging with people 11 High Low High More people involved More data available More accurate analytical and modeling methods High

12. Challenges: Successful Collaboration – Identifying how social media can support day-to-day interaction between police and residents – Analyzing and Extracting meaningful and actionable information from enormous data – Unstructured and unconstrained – Inferring actionable information – Quantifying behavior (emotions and linguistic attributes) – Maintaining responsiveness to residents – Promptness and timely action by police on social media – Engaging with people 12 High More accurate analytical and modeling methods Low High High More people involved More data available

13. Core Thesis Question How can social media platforms be utilized to support, analyze, and enhance day-to-day collaborative interaction between police and residents using computational methods? 13

14. Contributions – Identify need for social media support in collaborative policing – Quantify and mine unstructured data to analyzecommunication attributes and actionable information – Propose a method to enhancecollaboration – Police responsiveness usingrequest–response detection framework and police response quantification to residents – Images retrieval architecture for improved collective action using user generated content 14

15. Contributions – Identify need for social media support in collaborative policing – Quantify and mine unstructured data to analyzecommunication attributes and actionable information – Propose a method to enhance – Police responsiveness usingrequest–response detection framework and police response quantification to residents – Images retrieval architecture for improved collective action using user generated content 15

16. Research Problem Initial Coding Advanced Memo Theoretical Sampling new data Integrating for dimensions Need for Support : Requirement Elicitation – What opportunitiessocial media offers for supporting collaboration? – What challenges police and residents can face while adopting social media for collaboration? 16 • 17 Interviews • 200 Surveys • 20 Interviews • 402 Surveys Multi-stakeholder & Mixed Method Limited Grounded Theory Approach

17. Why Which For Whom Challenges 17 EDUCATED YOUNG ANYONE Need for Support : Requirement Elicitation

18. Police Officers Resident Communities Collaborative Platform Collaborative Ecosystem and Actors Interaction Layer Meaningful Information Acknowledgement / Response System Verification and Credibility Assessment Lessons 18

19. Contributions – Identify need for social media support in collaborative policing – Quantify and mine unstructured data to analyze communication attributes and actionable information – Propose a method to enhance – Police responsiveness usingrequest–response detection framework and police response quantification to residents – Images retrieval architecture for improved collective action using user generated content 19 High Low High More people involved More data available More accurate analytical and modeling methods High

20. Quantifying Interaction – Exploring the feasibility of social media in quantifying attributes of communication – Identifying behavioral attributes like affective expression, engagement and social and cognitive response processes 20 Resident to Resident Resident to Police Police to Resident Police to Police

21. Mixed Method Approach: Data Collection 21 85 Public and official Police Department Average age 3 years (from 2010 – April 2015) 47,474 wall posts and 85,408 status updates DT w/ ≥ 1 Comment P&C C Total DT 85,408 47,474 46,845 24,984 5,519 17,196 41,326 7,788 PP&C RP&C PC CC Quantitative Data Qualitative Analysis

22. Mixed Method Approach: Data Collection 22 85 Public and official Police Department Average age 3 years (from 2010 – April 2015) 47,474 wall posts and 85,408 status updates DT w/ ≥ 1 Comment P&C C Total DT 85,408 47,474 46,845 24,984 5,519 17,196 41,326 7,788 PP&C CP&C PC CC Quantitative Data 1600 comments on 255 posts Posts & Comments Collected public posts, 21 July - 21 Aug 2014 Qualitative Analysis

23. Quantifying Interaction for Meaningful Information – Content Cluster Identification – Nature of content and topics – Emotional Exchange Quantification – Nature of emotions and affective expression – Cognitive and Social Orientation Quantification – Type of linguistic attributes that characterize cognitive and social orientation – Engagement (Response) Quantification – Quantity and nature of engagement 23

24. Mixed Method Approach: Methods 24 Topics • Unigram (N) Gram Analysis • K-means Clusters with K-means++ seeding Emotional • Valence • Arousal Social and cognitive • Interpersonal Focus • Social Orientation • Cognition Engagement • No. of police and resident who comment in DTs • Distinct citizens who comment in DTs • Shannon’s Wiener Diversity index • Average no. of likes and comments LIWC and Anew Dictionary LIWC Dictionary Quantitative Data Thematic Inductive Analysis

25. Mixed Method Approach: Methods 25 Topics • Unigram (N) Gram Analysis • K-means Clusters with K-means++ seeding Emotional • Valence • Arousal Social and cognitive • Interpersonal Focus • Social Orientation • Cognition Engagement • No. of police and resident who comment in DTs • Distinct citizens who comment in DTs • Shannon’s Wiener Diversity index • Average no. of likes and comments LIWC and Anew Dictionary LIWC Dictionary Quantitative Data Thematic Inductive Analysis Validate and Characterize Type of sub-topics in Residents Posts Direct / Indirect Concerns + Style of communication Type of Engagement

26. 26 Unigram Freq. Unigram Freq. rules 0.015 safety 0.012 safety 0.014 following 0.011 violations 0.014 notice 0.010 challans 0.011 prosecuted 0.009 please 0.011 movement 0.008 citizens 0.01 complaint 0.008 Focus on advisories, the status of different cases being investigated (Mann Whitney U test, p < .05, z = −3.57) Most posts tend to request police to take action on their complaints Unigram Freq. Unigram Freq. please 0.026 people 0.022 take 0.021 please 0.02 action 0.019 one 0.019 people 0.019 take 0.016 one 0.019 action 0.015 time 0.017 time 0.015 Higher Reference to “people” Quantifying Interaction for Meaningful Information

27. K-Means++ Seeding: Clusters of Topics – Police initiated discussions are more focused than citizen initiated. 27 Awareness drive / safety campaigns Prosecuted / action taken reports Advisories on situations Newspaper articles Citizen tips and complaints Neighbourhood problems Missing people Appreciation Quantifying Interaction for Meaningful Information

28. Quantifying Interaction – Topic Identification – Nature of content and topics – Emotional Exchange Quantification – Nature of emotions and affective expression – Cognitive and Social Orientation Quantification – Type of linguistic attributes that characterize cognitive and social orientation – Engagement Quantification – Quantity and nature of engagement 28

29. – Negative sentiment higher in resident initiated threads than police 29 CP&C CC Avg Std. dev Avg Std. dev NA 0.021 0.03 0.018 0.04 Anx 0.001 0.01 0.003 0.02 Anger 0.006 0.02 0.005 0.02 Arousal 4.4 1.74 3.9 2.16 16.67% higher in CP&C 12.82% higher in CP&C Higher arousal and negative affect to be markers of sensitisationbecause of crime! Cp&c Cc Avg Std. dev Avg Std. dev NA 0.021 0.03 0.018 0.04 Anx 0.001 0.01 0.003 0.02 Anger 0.006 0.02 0.005 0.02 Arousal 4.4 1.74 3.9 2.16 200% higher in Cc (Mann-Whitney U, p < .01) (Mann-Whitney U p < .01) Meaningful Information Quantification: Emotions

30. – Discussion threads involving just the citizens are highly self-attention focused 30 Likely citizens mostly express their own concerns that they face with others CP&C CC ppron 0.062 0.059 0.045 0.056 i 0.008 0.017 0.014 0.033 shehe 0.002 0.01 0.003 0.003 they 0.005 0.013 0.008 0.008 75% More I have lived in the UK and all the time I have never heard anyone honking. …. if I see anyone who don't comply ? (U Test p < .01, z = −16.02) Meaningful Information Quantification: Social and Cognitive Orient.

31. Self-focused 31 My Vehicle KA-02-HW-3183 white color Honda Dio was stolen from Kadamba Hotel(Near Modi Hospital), RajajiNagar on Friday(25th July) evening between 6:30-7:45PM. Please help in tracing my vehicle. Dear BCP, though I stay at JP Nagar, but being part of KSFC Layout RWA (BanaswadiPolice station) , I got to know that there are frequent problem at KSFC Layout near BBMP Hall . . . ….

32. Accountability: Meaningful Information 32 Word Tree visualizations of posts in which residents questioned police using the word why.

33. Quantifying Interaction – Topic Identification – Nature of content and topics – Emotional Exchange Quantification – Nature of emotions and affective expression – Cognitive and Social Orientation Quantification – Type of linguistic attributes that characterize cognitive and social orientation – Engagement Quantification – Quantity and nature of engagement 33

34. Engagement Quantification – Content Generation 34 Police + Citizens 55,028 1,79,176 17,124 12,630 Citizens Only 54,982 1,79,176 17,081 12,630 Entropy 4.39 4.96 3.23 3.6 Police Resident 26% lower 10.28% lower Lower entropy: large number of comments are posted by a small number of citizens and police

35. Engagement (Response) Type 35 Ignored (#83) Acknowledged (21.3%) Reply Follow Up (10%) Dear X, We will take all possible legal measures in this regard. Thank you. Dear X, Please provide the police station details. Thank you. [Received no reply] Dear X, This post has been forwarded to appropriate Police Station…. Dear X, Please lodge a complaint at your nearest police station with the details ….. 44.3% 22% 172 posts

36. Lessons 36 Responsiveness Accountability Quantify And Extract Need for Enhancing Police Responsiveness

38. Serviceable Requests “A message that solicits a response in a form of an action or information from the police” 38Tucson Police. 2016. Calls for Service. https://www.tucsonaz.gov/police/terms. (May 2016). Low High High High More people involved More data available

39. Serviceable v/s Non-Serviceable Posts 39 Need more Information Forward Give Solution Cops driving wrong side [of road] near XXX hotel .. what action will be taken against them Date : 4/11/2015 (Wednesday), Time : 10:17 pm, Number : [withheld], Location : [withheld], Violations : Crossing line by way too much obstructing the vehicles which were coming from [withheld] entrance later he jumped the signal . Admin !! Can U Explain to me rules and regulations for transferring vehicle from Chennai to Bangalore? Ignored XXX shared NowThis Future's video. 21 February at 10:07 · BENGALURU CITY POLICE Interesting piece of handgun bullet-proof shield in development. Acknowledge Chennai City Traffic Police: a humble salute from a fellow Chennaiite for the commendable job in such rains!!

40. Research Questions: Serviceability – RQ 1: What attributes differentiates – Serviceable posts from non-serviceable requests and – Sub-types of serviceable requests w.r.t content characteristics such as linguistic and emotional attributes? – RQ 2: How does police response time vary between serviceable and non-serviceable posts made on social media? – RQ 3: Can machine learning techniques be used to automatically identify serviceable requests – Can we further classify them into different sub-types using post characteristics (content and metadata)? 40

41. Dataset 85 Public and official Police Department 22,213 wall posts 1000 Posts annotated by Police 41 Post Type #Posts Likes Comments Serviceable Posts Forward 286 1383 661 Give Solution 88 183 121 Thanks 72 1288 63 Need More Info. 104 1245 258 Total 550 4099 1103 Non-Serviceable Total 113 316 32 0.77 agreement using Fleiss Agreement

42. Attributes Emotion (Alchemy & LIWC) States: Anger, disgust, fear, joy, sadness Valence: Positive, negative, Anxiety Cognitive (LIWC) Cognitive Mechanism:Tentativeness and Discrepancy Inter- Personal (LIWC) 1st person singular & plural, 2nd person, 3rd person singular & plural, and impersonal pronouns Linguistic (LIWC) Objectivity, Tenses, Lexical Density & Parts- Of-Speech. Question Asked (Heuristics) who, how, why, what, where, whom and containing a “?” Entities (Alchemy) people, companies, organizations, cities, geographic features, facility, date andtime 42

43. Attributes – Top Topics Serviceable and Non-Serviceable Posts using LDA 43 LDA topic Vocabulary Traffic congestion Traffic, road, signal, bus, people,turn, jam Shared photos websites com, www, facebook, https, videos, traffic, http, type, old, photos, job Appreciation signal, great, good, taking, act, action Question posed asked, rules, vehicle, sir, said, car, know, what Places Telangana, state, hyderabad, city, nagar, Fines issued Challan [fine charged], violation, documents Cyber crime Police, city, cyber, crime nampally, complaint, better, safe

44. Attributes – Non-negative Matrix Factorization (NMF) for detecting closely connected topics in sub-types 44 NMF topic Vocabulary Police incorrect decision Police, asked, said, constable, taken, public wrong, driving, pay, vehicle, come, way Awareness Don’t, mobile, rules,need, people, let, share, helmet, circle, Dangerous driving complains wrong, dangerous, action, driving, turn, going junction Fines issued Vehicle, challan [fine charged], number, violation fine, documents, driving, guys, stopped, pay Parking issues Parking, people, bus, stop, parked, time, action Used Frobenius Norm

45. RQ1: Attributes Defining Serviceability – Serviceable requests show significantly higher value of negative emotional states 45 Serviceable Non-Serviceable Avg Std. dev Avg Std. dev Man. Anger 0.15 0.13 0.13 0.17 -3.43** Disgust 0.34 0.25 0.23 0.27 -3.88** Fear 0.24 0.21 0.15 0.18 -6.09** Sad 0.11 0.10 0.10 0.14 -5.45** +15.38% +60% Presumably, emotional states are experienced due to distress caused because of encounters with law and order situation.

46. RQ1: Attributes Defining Serviceability – Serviceable requests show significantly higher use of 1st person singular pronouns – highly self-attention 46 Serv. Non-Serv. Frwd Give Thanks Need 1st person Singular ** Avg. 1.68 1.54 1.61 2.56 0.70 1.80 Sd. 2.96 9.50 2.45 3.54 2.36 3.77 I am just worried if Hyderabad Traffic Police [HTP] makes things worse like always

47. RQ1: Attributes Defining Serviceability – Serviceable posts showed higher Objectivity 47 Serv. Non-Serv. Frwd Give Thanks Need Objectivity** Avg 2.86 2.04 +40% 3.47 2.64 2.07 1.9 Sd 2.84 2.63 3.16 2.8 2.6 1.29 Serv. Non-Serv. Frwd Give Thanks Need Past Tense Avg 1.75 0.81 1.88 1.68 0.78 2.14 Sd 2.99 2.87 2.86 3.55 2.13 3.23 Serviceable posts contain factual information on which the police can act upon. – Serviceable posts showed use of Past tense Cops were driving on the wrong side near [withheld] hotel .. what action was taken against them?

49. RQ2: Police Response Time – Survival Time – Time until the event of interest occurs – Censoring Event – Posts which did not receive a reply during our observation period – Survival Probability – Probability that a post survives longer than some specific time (t) given by survival function S(t) i.e. it does not receive a reply 49 Total N N of Events Censored %Censored Frwd 286 182 104 34.60 Give 88 53 35 39.80 Thanks 72 5 67 93.10 Need 104 60 44 42.30 Serv. 550 300 250 45.50 Police responses are maximum for Forward Sub-type posts

50. RQ2: Police Response Time 50 Mean Est. Sd. Error Median Est. Sd. Error Frwd 1062.52 82.22 21.33 2.05 Give 1064.37 138.08 20.43 8.45 Thank 2693.14 86.42 -- -- Need 1136.31 127.48 28.26 10.45 Serv. 1326.94 61.23 33.33 -- statistically significant difference between all four sub-types Log Rank (Mantel-Cox) test (χ2=57.03, df=3, p<0.005). Police reply to posts that can be given solution immediately followed by Forward – Kaplan Meier Estimator

52. Formulation 52

53. RQ3: Automatic Classification Performance: Cost weights – Ten-fold Cross Validation Performance of different algorithms to correctly identify serviceable posts. – Content attributes such as emotions and linguistic attributes are highly predictive of serviceable posts in addition to bag-of-words model 53 Algorithm Recall F1 Accuracy RF 0.97 0.85 0.87 LR 0.82 0.77 0.76 ADT 0.96 0.80 0.86 DT 0.84 0.78 0.77 GBC 0.94 0.83 0.84 Attributes R2 Deviance Emotion 0.23 437.88 Linguistic 0.19 401.83 Bag-of-words 0.53 260.07

54. – +Model 1 – Explains 15.6% of the variance – Reduces deviance significantly to 1,127.58 (178.14 less). – Better predictors are sadness, fear, and joy. – +Model 2 – Explains 20% of the variance – 1st person singular pronouns have statistically significant 54 RQ3: Automatic Classification Performance

55. – +Model 3 – Explains 26.3% of the variance – Reduces deviance significantly to 984.92. – Not helpful: Tenses (present and future) and lexical terms (verbs and adverbs) – +Model 4 – Explains 32.4% of the variance & deviance is 902.79 i.e. 82.13 less – Reliable predictors: question, date, time, and entity count – Topic does not help much 55 RQ3: Automatic Classification Performance

56. Serviceability Plugin 56

58. Challenges in similar images retrieval 58 Image processing 1. Scaling 2. Cropping 3. Stitching 4. Multiple

59. 59 Data Collection Sample images Event Total images Similar images Dissimilar images Charlie Hebdo 568 118 450 Kulkarni Ink 1,905 354 1,551 Insults Hanuman 664 277 387 ShaniShingnapur 180 70 110 RamRahim 408 97 311

60. Image features for similarity 1. Hand-Crafted Features a. 3D-colour histogram b. Daisy features c. ORB (Oriented FAST Rotated BRIEF) features d. Improved ORB (ORB + RANSAC) 60 2. Trainable Features a. Deep CNN (Convolution Neural Network) Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks.

61. Original Image Modified Image Improved ORB (% accuracy) CNN (% accuracy) Modification 93.7 98.3 Scaled, stitched image, added text, cropped 77.4 93.8 Cropped, stitched, text added 84.1 99.4 Scaled (7.4 ✕ 5.2) 61 Competing on modified images

62. PicHunt 62

63. Contributions – Identify need for social media support in collaborative policing – Quantify and mine unstructured data to analyzecommunication attributes and actionable information – Propose a method to enhancecollaboration – Police responsiveness usingrequest–response detection framework and police response quantification to residents – Images retrieval architecture for improved collective action using user generated content 63

64. Why it matters? – Propose a Data-Driven Technique to complement Overworked departments – Detecting posts that should elicit police response make social media streams more listenable for resident’s concerns – Help police improve policing and responsiveness – Taking cognizance of prominent constituents’ concern and unsafe regions can help police plan their resources better to provide improved safety – Measure resident’s reactions in a fine-grained manner – Information (e.g., emotions and interpersonal attributes) improve the understanding from factual information to a more nuanced understanding of psychological. 64

65. Technological Advancement – Designing early warning systems that indicate: – Need for emotional & social support needs to enhance police response to residents experiencing safety issues. – A feedback system on social media platforms that complements lack of physical signals of communication – informs about the likely time duration to respond to service request – Sense and record the reactions of citizens and share these records with decision makers – Take timely measures and gain better insights 65

66. Limitations – Cultural Limitation – Strong history of community policing may be helpful – Restricted Modality – Only text based serviceability detection – Consider other modalities such as videos, images etc. – Urban and Sub-urban Resident Communities – Rural areas may have different needs – Causality – Analysis is based on correlations 66

67. 67

68. Acknowledgement – Google for travel support – Tata Consultancy Services for funding the thesis work – All participants and police officers who helped us in various stages of the thesis – My collaborators – special thanks to Dr. Nitesh Saxena (UAB), Dr. Munmun De Choudhury (GaTech), Dr. Iulia Ion (Google) – Monitoring Committee Dr. Rahul Purandare, Dr. Sambuddho Chakravarty, Dr. Amarjeet Singh – Dr. Aditi Gupta, Dr. Paridhi Jain, Siddhartha Asthana, PrateekDewan, Anupama Aggarwal, Srishti Gupta, Rishabh Kaushal, Anuradha Gupta – Shrey Bagroy, Sonal Gupta, Divam Gupta, Megha Arora, Indira Sen, Neha Jawalkar, Bhavana Nagpal, Tushar Gupta, Vedant Swain – Members of Cybersecurity Education and Research Centre (CERC) and Precog who have given us continued support throughout the project – My Family 68

69. Acknowledgement – Dr. Aaditeshwar Seth – Dr. Carlos Castillo – Dr. Maura Conway – Dr. Ponnurangam Kumaraguru 69

70. Publications – Peer Reviewed Conferences – Sachdeva, N. and Kumaraguru, P. Online Social Media - New face of policing? A Sur- vey Exploring Perceptions, Behavior, Challenges for Police Field Officers and Residents. Accepted at18th International Conference on Human-Computer Interaction (HCII), 2016. – Sachdeva, N. and Kumaraguru, P. Deriving requirements for social media based community policing: insights from police. Accepted at ACM 16th International Digital Government Research Conference (dg.o 2015), 2015. – Sachdeva, N. and Kumaraguru, P. Online Social Networks and Police in India - Under- standing the Perceptions, Behavior, Challenges. Accepted at the European Conference on Computer-Supported Cooperative Work (ECSCW), 2015. – Sachdeva, N. and Kumaraguru, P. Characterising Behavior and Emotions on Social Media for Safety: Exploring Online Communication between Police and Citizens. Accepted at 30th British Human Computer Interaction Conference (HCI) 2016. – Goel, S., Sachdeva, N., Kumaraguru, P., Subramanyam, A., and Gupta, D. PicHunt: Social Media Image Retrieval for Improved Law Enforcement. Accepted at 8th International Conference on Social Informatics. 2016. – Sachdeva, N., and Kumaraguru, P. Social Networks for Police and Residents in India: Exploring Online Communication for Crime Prevention. Accepted at the ACM16th Annual International Conference on Digital Government Research (dg.o), 2015. [Best paper award]. – Sachdeva, N., and Kumaraguru, P. Call for Service: Characterizing and Modeling Police Response to Serviceable Requests on Facebook. Accepted at the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2017. 70

71. Publications – Peer-reviewed Conference Papers – Lamba, H., Bharadhwaj, V., Vachher, M., Agarwal, D., Arora, M., Sachdeva, N., Ku- maraguru, P. From Camera to Deathbed: Understanding Dangerous Selfies on Social Media. 11th International Conference on Web and Social Media (ICWSM), 2017 – Mohamed, M., Sachdeva, N., Georgescu, M., Gao, S., Saxena, N., Zhang, C., Kumaraguru, P., Van Oorschot, P., and Chen, W. A Three-Way Investigation of a Game-CAPTCHA: Automated Attacks, Relay Attacks and Usability. Accepted at 9th ACM Symposium on Information, Computer and Communications Security (ASIACCS), 2014. – Sachdeva, N., Saxena, N., and Kumaraguru, P. On the Viability of CAPTCHAs for Use in Telephony Systems: A Usability Field Study. 16th Information Security Conference November 2013 in Dallas, Texas (ISC), 2013. – Sachdeva, N., Saxena, N., and Kumaraguru, P. On the Viability of CAPTCHAs for Use in Telephony Systems: A Usability Field Study [Poster]. (APCHI) 2013 – Ion, I., Sachdeva, N., Kumaraguru, P., and Capkun, S. Home is safer than the cloud! privacy concerns for consumer cloud storage. In Symposium on Usable Privacy and Security (SOUPS) (2011). – Journal Papers – Manar Mohamed, Song G ao, Niharika Sachdeva, Nitesh Saxena, Chengcui Zhang, Pon- nurangam Kumaraguru and Paul van Oorschot. On the Security and Usability of Dynamic Cognitive Game CAPTCHAs. In Journal of Computer Security (JCS), 2017. 71

72. Thank you! niharikas@iiitd.ac.in http://precog.iiitd.edu.in/

Social Media and Policing: Computational Approaches to Enhancing Collaborative Action between Residents and Law Enforcement

Social Media and Policing: Computational Approaches to Enhancing Collaborative Action between Residents and Law Enforcement

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