Micro-blogging platforms like Twitter have become some of the most popular sites for people to share and express their views and opinions about public events like debates, sports events or other news articles. These social updates by people complement the written news articles or transcripts of events in giving the popular public opinion about these events. So it would be useful to annotate the transcript with tweets. The technical challenge is to align the tweets with the correct segment of the transcript. ET-LDA by Hu et al [9] addresses this issue by modeling the whole process with an LDA-based graphical model. The system segments the transcript into coherent and meaningful parts and also determines if a tweet is a general tweet about the event or it refers to a particular segment of the transcript. One characteristic of the Hu et al’s model is that it expects all the data to be available upfront and uses batch inference procedure. But in many cases we find that data is not available beforehand, and it is often streaming. In such cases it is infeasible to repeatedly run the batch inference algorithm. My thesis presents an online inference algorithm for the ET-LDA model, with a continuous stream of tweet data and compare their runtime and performance to existing algorithms.

With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data.

This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick.

In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information?

In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information.

The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied.

Browsing Twitter users, or browsers, often find it increasingly cumbersome to attach meaning to tweets that are displayed on their timeline as they follow more and more users or pages. The tweets being browsed are created by Twitter users called originators, and are of some significance to the browser who has chosen to subscribe to the tweets from the originator by following the originator. Although, hashtags are used to tag tweets in an effort to attach context to the tweets, many tweets do not have a hashtag. Such tweets are called orphan tweets and they adversely affect the experience of a browser.

A hashtag is a type of label or meta-data tag used in social networks and micro-blogging services which makes it easier for users to find messages with a specific theme or content. The context of a tweet can be defined as a set of one or more hashtags. Users often do not use hashtags to tag their tweets. This leads to the problem of missing context for tweets. To address the problem of missing hashtags, a statistical method was proposed which predicts most likely hashtags based on the social circle of an originator.

In this thesis, we propose to improve on the existing context recovery system by selectively limiting the candidate set of hashtags to be derived from the intimate circle of the originator rather than from every user in the social network of the originator. This helps in reducing the computation, increasing speed of prediction, scaling the system to originators with large social networks while still preserving most of the accuracy of the predictions. We also propose to not only derive the candidate hashtags from the social network of the originator but also derive the candidate hashtags based on the content of the tweet. We further propose to learn personalized statistical models according to the adoption patterns of different originators. This helps in not only identifying the personalized candidate set of hashtags based on the social circle and content of the tweets but also in customizing the hashtag adoption pattern to the originator of the tweet.

Although current urban search and rescue (USAR) robots are little more than remotely controlled cameras, the end goal is for them to work alongside humans as trusted teammates. Natural language communications and performance data are collected as a team of humans works to carry out a simulated search and rescue task in an uncertain virtual environment. Conditions are tested emulating a remotely controlled robot versus an intelligent one. Differences in performance, situation awareness, trust, workload, and communications are measured. The Intelligent robot condition resulted in higher levels of performance and operator situation awareness (SA).

Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics of newly emerged social media data present new challenges for social spammer detection. First, texts in social media are short and potentially linked with each other via user connections. Second, it is observed that abundant contextual information may play an important role in distinguishing social spammers and normal users. Third, not only the content information but also the social connections in social media evolve very fast. Fourth, it is easy to amass vast quantities of unlabeled data in social media, but would be costly to obtain labels, which are essential for many supervised algorithms. To tackle those challenges raise in social media data, I focused on developing effective and efficient machine learning algorithms for social spammer detection.

I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.

Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable

- in fact, the de facto - virtual town halls for people to discover, report, share and

communicate with others about various types of events. These events range from

widely-known events such as the U.S Presidential debate to smaller scale, local events

such as a local Halloween block party. During these events, we often witness a large

amount of commentary contributed by crowds on social media. This burst of social

media responses surges with the "second-screen" behavior and greatly enriches the

user experience when interacting with the event and people's awareness of an event.

Monitoring and analyzing this rich and continuous flow of user-generated content can

yield unprecedentedly valuable information about the event, since these responses

usually offer far more rich and powerful views about the event that mainstream news

simply could not achieve. Despite these benefits, social media also tends to be noisy,

chaotic, and overwhelming, posing challenges to users in seeking and distilling high

quality content from that noise.

In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context.

Enabled by EventRadar, it is more feasible to uncover patterns that have not been

explored previously and re-validating existing social theories with new evidence. As a

result, I am able to gain deep insights into how people respond to the event that they

are engaged in. The results reveal several key insights into people's various responding

behavior over the event's timeline such the topical context of people's tweets does not

always correlate with the timeline of the event. In addition, I also explore the factors

that affect a person's engagement with real-world events on Twitter and find that

people engage in an event because they are interested in the topics pertaining to

that event; and while engaging, their engagement is largely affected by their friends'

behavior.

As robotic technology and its various uses grow steadily more complex and ubiquitous, humans are coming into increasing contact with robotic agents. A large portion of such contact is cooperative interaction, where both humans and robots are required to work on the same application towards achieving common goals. These application scenarios are characterized by a need to leverage the strengths of each agent as part of a unified team to reach those common goals. To ensure that the robotic agent is truly a contributing team-member, it must exhibit some degree of autonomy in achieving goals that have been delegated to it. Indeed, a significant portion of the utility of such human-robot teams derives from the delegation of goals to the robot, and autonomy on the part of the robot in achieving those goals. In order to be considered truly autonomous, the robot must be able to make its own plans to achieve the goals assigned to it, with only minimal direction and assistance from the human.

Automated planning provides the solution to this problem -- indeed, one of the main motivations that underpinned the beginnings of the field of automated planning was to provide planning support for Shakey the robot with the STRIPS system. For long, however, automated planners suffered from scalability issues that precluded their application to real world, real time robotic systems. Recent decades have seen a gradual abeyance of those issues, and fast planning systems are now the norm rather than the exception. However, some of these advances in speedup and scalability have been achieved by ignoring or abstracting out challenges that real world integrated robotic systems must confront.

In this work, the problem of planning for human-hobot teaming is introduced. The central idea -- the use of automated planning systems as mediators in such human-robot teaming scenarios -- and the main challenges inspired from real world scenarios that must be addressed in order to make such planning seamless are presented: (i) Goals which can be specified or changed at execution time, after the planning process has completed; (ii) Worlds and scenarios where the state changes dynamically while a previous plan is executing; (iii) Models that are incomplete and can be changed during execution; and (iv) Information about the human agent's plan and intentions that can be used for coordination. These challenges are compounded by the fact that the human-robot team must execute in an open world, rife with dynamic events and other agents; and in a manner that encourages the exchange of information between the human and the robot. As an answer to these challenges, implemented solutions and a fielded prototype that combines all of those solutions into one planning system are discussed. Results from running this prototype in real world scenarios are presented, and extensions to some of the solutions are offered as appropriate.

Sarcasm is a nuanced form of language where usually, the speaker explicitly states the opposite of what is implied. Imbued with intentional ambiguity and subtlety, detecting sarcasm is a difficult task, even for humans. Current works approach this challenging problem primarily from a linguistic perspective, focusing on the lexical and syntactic aspects of sarcasm. In this thesis, I explore the possibility of using behavior traits intrinsic to users of sarcasm to detect sarcastic tweets. First, I theorize the core forms of sarcasm using findings from the psychological and behavioral sciences, and some observations on Twitter users. Then, I develop computational features to model the manifestations of these forms of sarcasm using the user's profile information and tweets. Finally, I combine these features to train a supervised learning model to detect sarcastic tweets. I perform experiments to extensively evaluate the proposed behavior modeling approach and compare with the state-of-the-art.

Recent efforts in data cleaning have focused mostly on problems like data deduplication, record matching, and data standardization; few of these focus on fixing incorrect attribute values in tuples. Correcting values in tuples is typically performed by a minimum cost repair of tuples that violate static constraints like CFDs (which have to be provided by domain experts, or learned from a clean sample of the database). In this thesis, I provide a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly. I thus avoid the necessity for a domain expert or master data. I also show how to efficiently perform consistent query answering using this model over a dirty database, in case write permissions to the database are unavailable. A Map-Reduce architecture to perform this computation in a distributed manner is also shown. I evaluate these methods over both synthetic and real data.

Twitter is a micro-blogging platform where the users can be social, informational or both. In certain cases, users generate tweets that have no "hashtags" or "@mentions"; we call it an orphaned tweet. The user will be more interested to find more "context" of an orphaned tweet presumably to engage with his/her friend on that topic. Finding context for an Orphaned tweet manually is challenging because of larger social graph of a user , the enormous volume of tweets generated per second, topic diversity, and limited information from tweet length of 140 characters. To help the user to get the context of an orphaned tweet, this thesis aims at building a hashtag recommendation system called TweetSense, to suggest hashtags as a context or metadata for the orphaned tweets. This in turn would increase user's social engagement and impact Twitter to maintain its monthly active online users in its social network. In contrast to other existing systems, this hashtag recommendation system recommends personalized hashtags by exploiting the social signals of users in Twitter. The novelty with this system is that it emphasizes on selecting the suitable candidate set of hashtags from the related tweets of user's social graph (timeline).The system then rank them based on the combination of features scores computed from their tweet and user related features. It is evaluated based on its ability to predict suitable hashtags for a random sample of tweets whose existing hashtags are deliberately removed for evaluation. I present a detailed internal empirical evaluation of TweetSense, as well as an external evaluation in comparison with current state of the art method.