Negative Link Prediction in Social Media

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework

Signed Link Analysis in Social Media Networks

Numerous real-world relations can be represented by signed networks with positive links (e.g., trust) and negative links (e.g., distrust). Link analysis plays a crucial role in understanding the link formation and can advance various tasks in social network analysis such as link prediction. The majority of existing works on link analysis have focused on unsigned social networks. The existence of negative links determines that properties and principles of signed networks are substantially distinct from those of unsigned networks, thus we need dedicated efforts on link analysis in signed social networks. In this paper, following social theories in link analysis in unsigned networks, we adopt three social science theories, namely Emotional Information, Diffusion of Innovations and Individual Personality, to guide the task of link analysis in signed networks.Comment: In the 10th International AAAI Conference on Web and Social Media (ICWSM-16

Community Detection over Social Media: A Compressive Survey

Social media mining is an emerging field with a lot of research areas such as, sentiment analysis, link prediction, spammer detection, and community detection. In today’s scenario, researchers are working in the area of community detection and sentiment analysis because the main component of social media is user. Users create different types of community in social world. The ideas and discussions in the community may be negative or positive. To detect the communities and their behavior researcher have done a lot of work, but still two major issues are presents per survey, Scalability and Quality of the community. These issues of community detection motivate to work in this area of social media mining. This paper gives a bird eye view over social media and community detection

PREDICTION IN SOCIAL MEDIA FOR MONITORING AND RECOMMENDATION

Social media including blogs and microblogs provide a rich window into user online activity. Monitoring social media datasets can be expensive due to the scale and inherent noise in such data streams. Monitoring and prediction can provide significant benefit for many applications including brand monitoring and making recommendations. Consider a focal topic and posts on multiple blog channels on this topic. Being able to target a few potentially influential blog channels which will contain relevant posts is valuable. Once these channels have been identified, a user can proactively join the conversation themselves to encourage positive word-of-mouth and to mitigate negative word-of-mouth. Links between different blog channels, and retweets and mentions between different microblog users, are a proxy of information flow and influence. When trying to monitor where information will flow and who will be influenced by a focal user, it is valuable to predict future links, retweets and mentions. Predictions of users who will post on a focal topic or who will be influenced by a focal user can yield valuable recommendations. In this thesis we address the problem of prediction in social media to select social media channels for monitoring and recommendation. Our analysis focuses on individual authors and linkers. We address a series of prediction problems including future author prediction problem and future link prediction problem in the blogosphere, as well as prediction in microblogs such as twitter. For the future author prediction in the blogosphere, where there are network properties and content properties, we develop prediction methods inspired by information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem, considering both network properties and content properties. We identify a number of features which have impact on prediction accuracy. For the future link prediction in the blogosphere, we compare multiple link prediction methods, and show that our proposed solution which combines the network properties of the blog with content properties does better than methods which examine network properties or content properties in isolation. Most of the previous work has only looked at either one or the other. For the prediction in microblogs, where there are follower network, retweet network, and mention network, we propose a prediction model to utilize the hybrid network for prediction. In this model, we define a potential function that reflects the likelihood of a candidate user having a specific type of link to a focal user in the future and identify an optimization problem by the principle of maximum likelihood to determine the parameters in the model. We propose different approximate approaches based on the prediction model. Our approaches are demonstrated to outperform the baseline methods which only consider one network or utilize hybrid networks in a naive way. The prediction model can be applied to other similar problems where hybrid networks exist

Network Representation Learning in Social Media

abstract: The popularity of social media has generated abundant large-scale social networks, which advances research on network analytics. Good representations of nodes in a network can facilitate many network mining tasks. The goal of network representation learning (network embedding) is to learn low-dimensional vector representations of social network nodes that capture certain properties of the networks. With the learned node representations, machine learning and data mining algorithms can be applied for network mining tasks such as link prediction and node classification. Because of its ability to learn good node representations, network representation learning is attracting increasing attention and various network embedding algorithms are proposed. Despite the success of these network embedding methods, the majority of them are dedicated to static plain networks, i.e., networks with fixed nodes and links only; while in social media, networks can present in various formats, such as attributed networks, signed networks, dynamic networks and heterogeneous networks. These social networks contain abundant rich information to alleviate the network sparsity problem and can help learn a better network representation; while plain network embedding approaches cannot tackle such networks. For example, signed social networks can have both positive and negative links. Recent study on signed networks shows that negative links have added value in addition to positive links for many tasks such as link prediction and node classification. However, the existence of negative links challenges the principles used for plain network embedding. Thus, it is important to study signed network embedding. Furthermore, social networks can be dynamic, where new nodes and links can be introduced anytime. Dynamic networks can reveal the concept drift of a user and require efficiently updating the representation when new links or users are introduced. However, static network embedding algorithms cannot deal with dynamic networks. Therefore, it is important and challenging to propose novel algorithms for tackling different types of social networks. In this dissertation, we investigate network representation learning in social media. In particular, we study representative social networks, which includes attributed network, signed networks, dynamic networks and document networks. We propose novel frameworks to tackle the challenges of these networks and learn representations that not only capture the network structure but also the unique properties of these social networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Online social capital : mood, topical and psycholinguistic analysis

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors\u27 mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being