Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Sentiment Analysis in Social Streams

In this chapter we review and discuss the state of the art on sentiment analysis in social streams –such as web forums, micro-blogging systems, and so- cial networks–, aiming to clarify how user opinions, affective states, and intended emotional effects are extracted from user generated content, how they are modeled, and how they could be finally exploited. We explain why sentiment analysis tasks are more difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the main- stream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Sentiment Analysis in Social Streams

In this chapter, we review and discuss the state of the art on sentiment analysis in social streams—such as web forums, microblogging systems, and social networks, aiming to clarify how user opinions, affective states, and intended emo tional effects are extracted from user generated content, how they are modeled, and howthey could be finally exploited.We explainwhy sentiment analysistasks aremore difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the mainstream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions

Social network usage is growing exponentially in the most up-to-date decade; though social networks are becoming increasingly popular every day, many users are continuously active social network users. Using Twitter, LinkedIn, Facebook, and other social media sites has become the most convenient way for people. There is an enormous quantity of data produced by users of social networks. The most common part of modern research analysis is instrumental for many social network analysis applications. However, people actively utilize social networking sites and diverse uses of these sites. social media sites handle an immense amount of knowledge and answer these three computational problems, noise, dynamism, and scale. Semantic comprehension of the document, image, and video exchanged in a social network was also an essential topic in network analysis. Utilizing data processing provides vast datasets such as averages, laws, and patterns to discover practical knowledge. Using social media, data analysis was primarily used for machine learning, analysis, information extraction, statistical modelling, data preprocessing, and data interpretation processes. This research intentions to deliver an inclusive overview of social network research and application analyze state-of-the-art social media data analysis methods by reviewing basic concepts, social networks and elements social network research is linked to. Semantic ways of manipulating text in social networks are then clarified, and literature discusses studies before on these themes. Next, the evolving methods in research on social network analysis are discussed, especially in analyzing semantic text on social networks. Finally, subjects and opportunities for future research directions are explained

ANALYZING IMAGE TWEETS IN MICROBLOGS

Ph.DDOCTOR OF PHILOSOPH

Network-aware recommendations in online social networks

Along with the rapid increase of using social networks sites such as Twitter, a massive number of tweets published every day which generally affect the users decision to forward what they receive of information, and result in making them feel overwhelmed with this information. Then, it is important for this services to help the users not lose their focus from what is close to their interests, and to find potentially interesting tweets. The problem that can occur in this case is called information overload, where an individual will encounter too much information in a short time period. For instance, in Twitter, the user can see a large number of tweets posted by her followees. To sort out this issue, recommender systems are used to give contents that match the user's needs. This thesis presents a tweet-recommendation approach aiming at proposing novel tweets to users and achieving improvement over baseline. For this reason, we propose to exploit network, content, and retweet analyses for making recommendations of tweets. The main objective of this research is to recommend tweets that are unseen by the user (i.e., they do not appear in the user timeline) because nobody in her social circles published or retweeted them. To achieve this goal, we create the user's ego-network up to depth two and apply the transitivity property of the \emph{friends-of-friends} relationship to determine interesting recommendations. After this step, we apply cosine similarity and Jaccard distance as similarity measures for the candidate tweets obtained from the network analysis using bigrams. We also count the mutual retweets between the ego user and candidate users as a measure of shared similar tastes. The values of these features are compared together for each of the candidate tweets using pairwise comparisons in order to determine interesting recommendations that are ranked to best match the user's interests. Experimental results demonstrate through a real user study that our approach improves the state-of-the-art technique. In addition to the efficiency of our approach in finding relevant contents, it is also characterized by the fact of providing novel tweets, which solves the over-specialization challenge or serendipity problem that appears when using content-based recommender systems as a stand alone approach of recommendation

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks