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The research on social networks has advanced significantly, which can be attributed to the prevalence of the online social websites and instant messaging systems as well as the popularity of mobile apps that support easy access to online social networks. These social networks are usually characterized by the complex network structures and rich contextual information. They now become the key platforms for, among others, content dissemination, professional networking, recommendation, alerting, and political campaigns. As online social network users perform activities on the social networks, they leave data traces of human behavior which allow the latter to be studied at scale. There are however a wide range of challenges in analyzing human behavior in social networks. Behavior analysis in online social networks spans a number of disciplines, across numerous fields in and beyond computer science. For example, one would have to involve social network analysis, an area in social science, to analyze social relationships, how they evolve and mature over time. The results of behavior analysis have important implications on community discovery, anomaly detection, and trend prediction, and they can enhance applications in multiple domains such as information retrieval, recommendation systems, and trust and security. Research in behavior analysis is a fertile ground also for businesses and IT industry, as they develop innovative ideas fostering the design of the new generation of social network platforms and their services.

Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the author's behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base. © 2012 ACM.

The popularity of job websites has significantly changed the way people learn about different occupations. Among the insights offered by these websites are the statistics of occupation salaries which are useful information for job seekers, career coaches, graduating students, and labor related government agencies. Such statistics include the distribution of job salaries of each occupation, such as average or quantiles. However, significant variability in salary (and review salary) can be found among jobs of the same occupation as we gather job post and review data from job websites. Such variability shows the existence of biases, including salary competitiveness in job posts and salary inflation in job reviews. Based on the observation, we aim at developing an approach to derive occupation salary for a job market, named unbiased salary, by aggregating offer salaries from job posts and review salaries from review data and at the same time removing their biases. To achieve this goal, we proposed COC-model to learn unbiased salaries of occupations, competitiveness of companies and inflation of companies efficiently. COC here is an abbreviation of ''Company, Occupation, Company'', which represents two different connections between companies and occupations from job posting site and job review site. COC-model represents the dependency of salary information between companies and occupations in job post data and job review data. It begins with defining three latent variables, say competitiveness, inflation, and unbiased salary, based on their dependencies. Instead of computing these variables iteratively, we formulate the interaction among these three latent variables into a matrix form so that these values could be then efficiently learned in a unified way by a series of matrix operations. Extensive experiments are conducted, including empirical studies about competitiveness and inflation of companies using real dataset and performance testing by synthetic dataset. The experimental results show that COC-model can not only derive unbiased salaries effectively but also help us to understand latent biases in job post and job review data.

Conversations on social media networks that discuss a crisis incident as it unfolds have become a norm in recent years. Left to its own devices, such conversations could quickly degenerate into rumor mills. Little research has thus far examined the correction of rumors on social media. Using the thirdperson effect as a theoretical underpinning, we developed a model of collective rumor correction on social media based on an incident surrounding the death hoax of a political figure. Tweets from Twitter were collected and analyzed for the period when a spike of circulating rumors speculating the demise of Singapore's first prime minister was detected. Corrections of the rumor also went viral on the same day. Our study reveals that corrective behavior during a death hoax situation on Twitter is characterized by affirmative and rational rebuttals verifiable by credible sources. While the inclusion of credible sources is essential for both rumor diffusion and corrections, correcting a rumor differs from its diffusion in that unambiguity and low emotional levels are crucial. Key characteristics of collective rumor correction identified by this study have implications for both theory and practice. We discussed these implications together with the study's limitations and suggestions for future research.

Social media users make decisions about what content to post and read. As posted content is often visible to others, users are likely to impose self-censorship when deciding what content to post. On the other hand, such a concern may not apply to reading social media content. As a result, the topics of content that a user posted and read can be different and this has major implications to the applications that require personalization. To better determine and profile social media users' topic interests, we conduct a user survey in Twitter. In this survey, participants chose the topics they like to post (posting topics) and the topics they like to read (reading topics). We observe that users' posting topics differ from their reading topics significantly. We find that some topics such as "Religion", "Business" and "Politics" attract much more users to read than to post. With the ground truth data obtained from the survey, we further explore the discovery of users' posting and reading topics separately using features derived from their posted content, received content and social networks.

Wireless sensor networks have been widely used in civilian and military applications. Primarily designed for monitoring purposes, many sensor applications require continuous collection and processing of sensed data. Due to the limited power supply for sensor nodes, energy efficiency is a major performance concern in query processing. In this paper, we focus on continuous kNN query processing in object tracking sensor networks. We propose a localized scheme to monitor nearest neighbors to a query point. The key idea is to establish a monitoring area for each query so that only the updates relevant to the query are collected. The monitoring area is set up when the kNN query is initially evaluated and is expanded and shrunk on the fly upon object movement. We analyze the optimal maintenance of the monitoring area and develop an adaptive algorithm to dynamically decide when to shrink the monitoring area. Experimental results show that establishing a monitoring area for continuous kNN query processing greatly reduces energy consumption and prolongs network lifetime.

Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. In these applications, continuous query processing is often required and their efficient evaluation is a critical requirement to be met. Due to the limited power supply for sensor nodes, energy efficiency is a major performance measure in such query evaluation. In this paper, we focus on continuous kNN query processing. We observe that the centralized data storage and monitoring schemes do not favor energy efficiency. We therefore propose a localized scheme to monitor long running nearest neighbor queries in sensor networks. The key idea is to establish a monitoring area for each query so that only the updates relevant to the query are collected. Experimental results show that our scheme outperforms the centralized scheme in terms of energy efficiency and network lifetime.

Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. The sensor nodes in the network have the abilities to sense, store, compute and communicate. To enable object tracking applications, spatial queries such as nearest neighbor queries are to be supported in these networks. The queries can be injected by the user at any sensor node. Due to the limited power supply for sensor nodes, energy efficiency is the major concern in query processing. Centralized data storage and query processing schemes do not favor energy efficiency. In this paper, we propose a distributed scheme called DNN for in-network processing of nearest neighbor queries. A cost model is built to analyze the performance of DNN. Experimental results show that DNN outperforms the centralized scheme significantly in terms of energy consumption and network lifetime.

With the widespread adoption of the Web, many companies and organizations have established websites that provide information and support online transactions (e.g., buying products or viewing content). Unfortunately, users have limited attention to spare for interacting with online sites. Hence, it is of utmost importance to design sites that attract user attention and effectively guide users to the product or content items they like. Thus, we propose a novel and scalable experimentation approach to evaluate the effectiveness of online site designs. Our case study focuses on the effects of an authority message on visitors' browsing behavior on workshop and seminar online announcement sites. An authority message emphasizes a particular prominent speaker and his/her achievements. Through dividing users into control and treatment groups and carefully tracking their online activities, we observe that the authority message influences the way users interact with page elements on the website and increases their interests in the authority speakers.

This article seeks to examine the relationships among source credibility, message plausibility, message type (rumor or rumor correction) and retransmission of tweets in a rumoring situation. From a total of 5,885 tweets related to the rumored death of the founding father of Singapore Lee Kuan Yew, 357 original tweets without an "RT" prefix were selected and analyzed using negative binomial regression analysis. The results show that source credibility and message plausibility are correlated with retransmission. Also, rumor correction tweets are retweeted more than rumor tweets. Moreover, message type moderates the relationship between source credibility and retransmission as well as that between message plausibility and retransmission. By highlighting some implications for theory and practice, this article concludes with some limitations and suggestions for further research.

In political contexts, it is known that people act as "motivated reasoners", i.e., information is evaluated first for emotional affect, and this emotional reaction influences later deliberative reasoning steps. As social media becomes a more and more prevalent way of receiving political information, it becomes important to understand more completely the interaction between information, emotion, social community, and information-sharing behavior. In this paper, we describe a high-precision classifier for politically-oriented tweets, and an accurate classifier of a Twitter user's political affiliation. Coupled with existing sentiment-analysis tools for microblogs, these methods enable us to systematically study the interaction of emotion and sharing in a large corpus of politically-oriented microblog messages, collected from just before the 2012 US presidential election. In particular, we seek to understand how information sharing is influenced by the political affiliation of the sender and receiver of a message, and the sentiment associated with the message.