Twitter sentiment analysis has become a hot research topic in recent years. Most of existing solutions to Twitter sentiment analysis basically only consider textual information of Twitter messages, and struggle to perform well when facing short and ambiguous Twitter messages. Recent studies show that sentiment diffusion patterns on Twitter have close relationships with sentiment polarities of Twitter messages. Therefore, in this paper, we focus on how to fuse textual information of Twitter messages and sentiment diffusion patterns to obtain better performance of sentiment analysis on Twitter data. To this end, we first analyze sentiment diffusion by investigating a phenomenon called sentiment reversal, and find some interesting properties of sentiment reversals. Then, we consider the inter-relationships between textual information of Twitter messages and sentiment diffusion patterns, and propose an iterative algorithm called SentiDiff to predict sentiment polarities expressed in Twitter messages. To the best of our knowledge, this work is the first to utilize sentiment diffusion patterns to help improve Twitter sentiment analysis. Extensive experiments on real-world dataset demonstrate that compared with state-of-the-art textual information based sentiment analysis algorithms, our proposed algorithm yields PR-AUC improvements between 5.09 and 8.38 percent on Twitter sentiment classification tasks.

With the blossoming of social networking platforms like Twitter and Facebook, how to infer the opinions of online social network users on specific topics they had not directly given yet, has received much attention. Existing solutions mainly rely on one's previous posted messages. However, recent studies show that over 40% of users opt to be silent all or most of the time and post very few messages. Consequently, the performance of existing solutions will drop dramatically when they are applied to infer silent users' opinions, and how to infer the opinions of these silent users becomes a meaningful while challenging task. Inspired by the collaborative filtering techniques in cold-start recommendations, we infer the opinions of silent users by leveraging the text content posted by active users and their relationships between silent users. Specifically, we first consider both observed and pseudo relationships among users, and cluster users into communities in order to extract various kinds of features for opinion inference. We then design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features. Extensive experiments on real-world data from Twitter show that our CSMF model achieves over 80% accuracy for the inference of silent users' opinions.

With the rapid development of various online video sharing platforms, large numbers of videos are produced every day. Video affective content analysis has become an active research area in recent years, since emotion plays an important role in the classification and retrieval of videos. In this work, we explore to train very deep convolutional networks using ConvLSTM layers to add more expressive power for video affective content analysis models. Network-in-network principles, batch normalization, and convolution auto-encoder are applied to ensure the effectiveness of the model. Then an extended emotional representation model is used as an emotional annotation. In addition, we set up a database containing two thousand fragments to validate the effectiveness of the proposed model. Experimental results on the proposed data set show that deep learning approach based on ConvLSTM outperforms the traditional baseline and reaches the state-of-the-art system.

The task of identifying influential spreaders for various big data social network applications plays a crucial role in social networks, and lays the foundation for predictive or recommended applications. Though there are several kinds of methods for this task, most of these methods exploit global computing, and are time-consuming for large-scale social networks. In this paper, by combining the degree centrality with the law of universal gravitation in physics, we present a novel metric called Logarithm Gravity (LG) centrality to quantify the influence of nodes in large-scale social networks, which views the value of the degree centrality as mass for each node and regards the length of the shortest path between a pair of nodes as their distance. In our model, for each node, a local network is generated by obtaining all nodes, which are less than k-hop from it. Then the sum of mutual influence values between the node in question and all other nodes in each local network is figured out as its LG centrality index. Therefore, the complexity of our approach is scalable by adjusting the value of k with efficient local computation. We compare our LG centrality with k-shell, betweenness and degree centralities. Experimental evidence, which has been collected based on the SIR model with four real-world datasets, shows that our approach is more feasible and effective than other state-of-art methods in terms of infection ratios and computational complexity.

The last decade has witnessed a dramatic growth of social networks, such as Twitter, Sina Microblog, etc. Messages/short texts on these platforms are generally of limited length, causing difficulties for machines to understand. Moreover, it is rarely possible for users to read and understand all the content due to the large quantity. So it is imperative to cluster and extract the viewpoints of these short texts. To solve this, the representation of a word is enriched with additional features from external, but it is demanding in terms of computational and time resources. In this paper, we proposed OnSeS, a novel short text summarization method which makes full use of word2vec to represent a word and utilizes neural network model to generate each word of the summary. OnSeS consists of three phrases: 1) clustering short texts using the K-means algorithm; 2) ranking content of each cluster by building a graph-based ranking model using BM25; 3) generating main point of each cluster with the help of neural machine translation model on the top ranked sentence. The experimental results reveal that our proposed fully data-driven approach outperforms state-of-the-art method.

Online short texts of hot topics submitted to social media by users can provide valuable personal opinions, which are useful for service providers and individuals. However, it is difficult for readers to grasp the main opinions of massive short texts. In this paper, to cope with the summarization challenge of short texts, we proposed a novel approach, which makes full use of BM25 to weight each short text and syntactic parsing to generate important information of each opinion cluster. The approach also utilizes the feature pruning to reduce the dimensions of the vectors. We conduct our experiments on real datasets and evaluate the results by standard metrics and manual evaluation. The experimental results show that our proposed approach improves the accuracy when compared to the state-of-the-art method.

This paper proposes to detect event coverage and determines event sources. Generally, an appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph. Event sources are determined which correspond to the barycenters in this graph. Experiments show that this technique is more energy efficient, especially when the network topology is relatively steady.

Identifying the most influential spreaders in social networks has many practical applications. The existing methods for the purpose are either too time-consuming for dynamic large-scale networks, such as betweenness centrality, closeness centrality, eigenvector centrality and Katz centrality, or do not consider the network topology, such as degree centrality. To design an effective method to identify the most influential nodes in a network, we propose a novel metric, k-hop centrality which is a generalization of degree centrality. The k-hop index is the summation of the number n(i) of nodes within k-hop distance from the node in question, attenuated by 1/αi, for 1 ≤ i ≤ k (α is the average degree of nodes in the network). It is calculated in a localized manner and is complexity-scalable by adjusting the value of k, thus suitable for dynamically changing, large social networks. We adopt the Susceptible Infected Recovered (SIR) model to evaluate the performance of k-hop centrality over four real datasets of complex networks, and experimental results show that our method outperforms state-of-the-art methods in this field in terms of both infection ratios (spreading influence) and computational complexity. Our work sheds some light on designing efficient spreading strategies for complex networks.