The way people communicate — be it verbally, visually, or via text– is indicative of personality traits. In social media the concept of the status update is used for individuals to communicate to their social networks in an always-on fashion. In doing so individuals utilize various kinds of speech acts that, while primarily communicating their content, also leave traces of their personality dimensions behind. We human-coded a set of Facebook status updates from the myPersonality dataset in terms of speech acts label and then experimented with surface level linguistic features including lexical, syntactic, and simple sentiment detection to automatically label status updates as their appropriate speech act. We apply supervised learning to the dataset and using our features are able to classify with high accuracy two dominant kinds of acts that have been found to occur in social media. At the same time we used the coded data to perform a regression analysis to determine which speech acts are significant of certain personality dimensions. The implications of our work allow for automatic large-scale personality identification through social media status updates.

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

Current travel recommendation systems are helpful in addressing a traveler's information needs to certain extent, however, most of them fail to factor in the user in their recommendations. TripEneer proposes travel recommendations to a traveler by keeping the user preferences and constraints as first class citizens. We present an intuitive UI for helping users plan their travel trips quickly and easily. In the current demo we present various global and user-specific ranking models used for recommending travel destinations. Our preliminary evaluation showed that the users found the personalized recommendations, based on the user model, most useful.

We demonstrate a portable and functional Internet-connected robotics system called TwitterViz, which visualizes real-time Twitter data on a kinetic sculpture. The purpose of our project is to explore how robotics can 'understand' and visualize remote data streams. We have constructed an overall system architecture with custom hardware and software that drives a robotic sculpture in real-time. Our system monitors Twitter data from the public API feed, analyzes the Tweets, and then converts the Tweets to motion on a kinetic robot. Our live demonstration of the TwitterViz robotics system fits onto a desktop, and includes the functioning kinetic robot, mini-ITX server, display for raw Tweets, and 4G connectivity to communicate with the Twitter API.

Community-based question-answering (CQA) services contribute to solving many difficult questions we have. For each question in such services, one best answer can be designated, among all answers, often by the asker. However, many questions on typical CQA sites are left without a best answer even if when good candidates are available. In this paper, we attempt to address the problem of predicting if an answer may be selected as the best answer, based on learning from labeled data. The key tasks include designing features measuring important aspects of an answer and identifying the most importance features. Experiments with a Stack Overflow dataset show that the contextual information among the answers should be the most important factor to consider.

Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full network, even though they require much less data and computational resources. Further, our evidence suggests that locality of preference, or the non-random distribution of item preferences in a social network, is a driving force behind the value of incorporating social network information into recommender algorithms. When locality is high, as in Twitter data, simple k-nn recommenders do better based only on friends than they do if they draw from the entire network. These results help us understand when, and why, social network information is likely to support recommendation systems, and show that systems that see ego-centric slices of a complete network (such as websites that use Facebook logins) or have computational limitations (such as mobile devices) may profitably use ego-centric recommendation algorithms.

Location-based services often use only a single mobility data source, which typically will be scarce for any new user when the system starts out. We propose a transfer learning method to characterize the temporal distribution of places of individuals by using an external, additional, large-scale check-in data set such as Foursquare data. The method is applied to the next place prediction problem, and we show that the incorporation of additional data through the proposed method improves the prediction accuracy when there is a limited amount of prior data.

Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target pairs. This information is then used to predict the sentiment of a previously unknown user towards another user or an entity using collaborative filtering algorithms. We validate our model on two Twitter datasets using different collaborative filtering techniques. Our preliminary results demonstrate that the proposed approach can be effectively used in twitter sentiment prediction, thus mitigating the data sparsity problem.

Providing a convenient mechanism for accessing the Internet, smartphones have led to the rapid growth of Social Networking Services (SNSs) such as Twitter and have served as a major platform for SNSs. Nowadays, people are able to check conveniently the SNS messages posted by their friends and followers via their smartphones. As a consequence, people are exposed to spoilers of TV programs that they follow. So far, there are two previous works that explored the detection of spoilers in texts, not SNS: (1) keyword matching method and (2) machine-learning method based on Latent Dirichlet Allocation (LDA). The keyword matching method evaluates most tweets as spoilers; hence its poor recall performance. The other method based on LDA, although successful on large text, works poorly on short segments of text such as those found on Twitter and evaluates most tweets as non-spoilers. This paper presents four features that are significant in the classification of spoiler tweets. Using those features, we classified spoiler tweets pertaining to a reality TV show (“Dancing with the Stars”). We experimentally compared our method with previous methods, with our method achieving substantially higher precision compared to the keyword matching and LDA-based methods while maintaining comparable recalls.

We investigate the structural patterns of the appearance and disappearance of links in dynamic knowledge networks. Human knowledge is nowadays increasingly created and curated online, in a collaborative and highly dynamic fashion. The knowledge thus created is interlinked in nature, and an important open task is to understand its temporal evolution. In this paper, we study the underlying mechanisms of changes in knowledge networks which are of structural nature, i.e., which are a direct result of a knowledge network's structure. Concretely, we ask whether the appearance and disappearance of interconnections between concepts (items of a knowledge base) can be predicted using information about the network formed by these interconnections. In contrast to related work on this problem, we take into account the disappearance of links in our study, to account for the fact that the evolution of collaborative knowledge bases includes a high proportion of removals and reverts. We perform an empirical study on the best-known and largest collaborative knowledge base, Wikipedia, and show that traditional indicators of structural change used in the link analysis literature can be classified into four classes, which we show to indicate growth, decay, stability and instability of links. We finally use these methods to identify the underlying reasons for individual additions and removals of knowledge links.