Social media and policing : computational approaches to enhancing collaborative action between residents and law enforcement

Abstract

Law and order concerns are one of the major disquiets of urban societies in day-to-day life. Various crime prevention theories show the importance of collaboration between residents and police for maintaining law and order and addressing concerns. Collaborative action across public organizations such as police shows different challenges like enabling collective action, problem-solving, accountability, and responsiveness of the organizational actors towards residents. To enable collective action and problem solving with the help of residents, modern police departments explore innovative mechanisms to overcome the barrier of reachability and communication. Using these mechanisms, residents can convey their concerns and enquire/ provide information useful for police contributing towards the collaborative process. With growing reach of web 2.0, social media has emerged as an effective platform to enable collaboration between police and resident. Social media use for communication between police and resident introduces various challenges for organizations. Owing to the massive volume of content on social media, the responsiveness of the police to the online content determine the overall success of the collaborative efforts. Additionally, social media raises challenges such as inferring actionable information and quantifying behavior (like emotions and other linguistic attributes) from unconstrained natural language text. Police organizations seek the support of technology and automation to address these challenges. Several researchers have examined the efficacy of collaborating through social media in a diverse set of scenarios like crises (natural and man-made) and socio-political upheavals. Despite its usefulness in crises, social media role to enable police in collective action and responding to day-to-day concerns of residents remains largely unexplored.

Therefore, we believe it is important to understand, analyze and enhance the opportunity for collaboration between police and residents using day-to-day life using social media. In this thesis, we study collaborative efforts by police on social media in the specific context of India. The policing department in India has only 130 personnel per 100,000 residents which is much lower than the UN recommended 270–280 personnel per 100,000 residents [35, 82]. Given the constraint on number of officers, police in India have felt the need to obtain community based collaboration to accomplish its increasingly vast duties [35]. Social media popularity among residents has motivated the police to use it for day-to-day interactions and improved community policing. Based on this understanding, we define the core research question of this thesis as how can a platform such as social media be utilized to support, analyze, and enhance collaborative action by police organization and residents? This thesis makes following contributions towards the core research question: a) Investigate the current role of social media in supporting police and residents’ collaboration for community policing and collective action, b) Mine and quantify unstructured data on social media for managing actionable information and understanding societal beliefs affecting day-to-day policing for improved collective action, and c) Develop a framework for extracting “serviceable requests” from social media to enhance engagement among residents and predicting expected police response to such requests. To answer the first research question of this thesis, we conduct semi-structured interviews and surveys of both the stake-holders (police and residents) to understand social media usage requirements. Through our analysis, we highlight residents and police thoughts about information shared, need for measures to handle offensive comments, and acknowledgment overload for police on social media. Following this, we adopt a mixed method approach (qualitatively and quantitatively analysis) for analyzing the content generated on police profiles on social media. We perform this analysis along multiple dimensions: content attributes, meta-data (likes and comments), image attributes, and police response time. Our results show that residents post information (e.g., location) about various crimes such as neighborhood issues, financial frauds, and thefts. Police response to residents’ post varies from ‘reply’, ‘acknowledge’, ‘follow-up’, and ‘ignore’. We demonstrate that using statistical, unsupervised, and bag-of-words LIWC methods; we can quantify interaction patterns/ cues and translate them into features that can be helpful for developing frameworks to corroborate collective intelligence. We also present a real-time image search system using Convolution Neural Networks (CNN) which retrieves modified images that allow first responders such as police to analyze the current spread of images, sentiments floating, and details of users propagating such content. The system aids officials to save the time of manually analyzing the content as it reduces the search space on an average by 67%. Finally, towards our third research goal, this thesis proposes a request–response detection framework for identifying resident’s posts that elicit police response (called serviceable posts), based on the input of police experts. Our observations show a decrease in police response time of the serviceable requests that can be immediately resolved, thus suggesting that successful identification of serviceable posts may ultimately result in systems that facilitate in extending timely police support and improve police responsiveness towards residents. Lastly, we evaluate a series of statistical models to predict serviceable posts and its different types.