The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes, through graph representation, to deep learning-based approaches. Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

Discovering social relations, e.g., kinship, friendship, etc., from visual contents can make machines better interpret the behaviors and emotions of human beings. Existing studies mainly focus on recognizing social relations from still images while neglecting another important media--video. On one hand, the actions and storylines in videos provide more important cues for social relation recognition. On the other hand, the key persons may appear at arbitrary spatial-temporal locations, even not in one same image from beginning to the end. To overcome these challenges, we propose a Multi-scale Spatial-Temporal Reasoning (MSTR) framework to recognize social relations from videos. For the spatial representation, we not only adopt a temporal segment network to learn global action and scene information, but also design a Triple Graphs model to capture visual relations between persons and objects. For the temporal domain, we propose a Pyramid Graph Convolutional Network to perform temporal reasoning with multi-scale receptive fields, which can obtain both long-term and short-term storylines in videos. By this means, MSTR can comprehensively explore the multi-scale actions and storylines in spatial-temporal dimensions for social relation reasoning in videos. Extensive experiments on a new large-scale Video Social Relation dataset demonstrate the effectiveness of the proposed framework.

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Social Relation Recognition From Videos via Multi-Scale Spatial-Temporal Reasoning

Different from general face recognition, age-invariant face recognition (AIFR) aims at matching faces with a big age gap. Previous discriminative methods usually focus on decomposing facial feature into age-related and age-invariant components, which suffer from the loss of facial identity information. In this article, we propose a novel Multi-feature Fusion and Decomposition (MFD) framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants. Specifically, we first sample multiple face images of different ages with the same identity as a face time sequence. Then, the multi-head attention is employed to capture contextual information from facial feature series, extracted by the backbone network. Next, we combine feature decomposition with fusion based on the face time sequence to ensure that the final age-independent features effectively represent the identity information of the face and have stronger robustness against the aging process. Besides, we also mitigate imbalanced age distribution in the training data by a re-weighted age loss. We experimented with the proposed MFD over the popular CACD and CACD-VS datasets, where we show that our approach improves the AIFR performance than previous state-of-the-art methods. We simultaneously show the performance of MFD on LFW dataset.

Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention

Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.

Leveraging Joint Interactions for Credibility Analysis in News Communities with Continuous Conditional Random Field

Multiple modalities represent different aspects by which information is conveyed by a data source. Modern day social media platforms are one of the primary sources of multimodal data, where users use different modes of expression by posting textual as well as multimedia content such as images and videos for sharing information. Multimodal information embedded in such posts could be useful in predicting their popularity. To the best of our knowledge, no such multimodal dataset exists for the prediction of social media photos. In this work, we propose a multimodal dataset consisiting of content, context, and social information for popularity prediction. Speci?cally, we augment the SMPT1 dataset for social media prediction in ACM Multimedia grand challenge 2017 with image content, titles, descriptions, and tags. Next, in this paper, we propose a multimodal approach which exploits visual features (i.e., content information), textual features (i.e., contextual information), and social features (e.g., average views and group counts) to predict popularity of social media photos in terms of view counts. Experimental results con?rm that despite our multimodal approach uses the half of the training dataset from SMP-T1, it achieves comparable performance with that of state-of-the-art.

A Multimodal Approach to Predict Social Media Popularity

This paper presents the method that underlies our submission to the popularity prediction task of Social Media Prediction Challenge 2017. The task is designed to predict the impact of sharing different posts for a publisher on social media. There are many factors that influence image popularity; these include not only the visual features of the image, but also the social features, such as user characteristics of its poster and even the upload time. In this project, we propose a fast and effective framework for popularity prediction. First, we investigate and extract visual and social features of images. For the visual feature, we introduce 1) global feature descriptors, such as Local Binary Pattern and Color Names, 2) local feature descriptors, such as Local Maximal Occurrence, and 3) deep features. For the social feature, we adopt users features (average views, group count, and member count), post features (title length, description length, and tag count), and time features (month, weekday, day, and hour). Furthermore, we fed a fusion of multi-feature to Linear Regression, Matrix Factorization based on Time and feature Cluster, and Support Vector Regression models respectively, and present comparative analysis of the prediction results. Finally, we choose the best model to predict the popularity scores of the test images. Experimental results demonstrate that our method can achieve 0.8581, 1.4062 and 0.8625 in terms of Spearman Ranking Correlation, Mean Absolute Error, and Mean Squared Error, respectively.

Multi-feature Fusion for Predicting Social Media Popularity

Social relations are ubiquitous in people’s daily life. Especially, the widespread of video in social media and intelligent surveillance gives us a new chance to discover the social relations among people. Previous researches mostly focus on the recognition of social relations from texts, blogs, or images. However, these methods are only concentrated on limited social relations and incapable of dealing with video data. In this paper, we address the challenges of social relation recognition by employing a multi-stream model to exploit the abundant multimodal information in videos. First of all, we build a video dataset with 16 categories of social relations annotation according to psychology and sociology studies, named Social Relation In Videos (SRIV), which comprises of 3,124 videos. According to our knowledge, it is the first video dataset for the social relation recognition. Secondly, we propose a multi-stream deep learning model as a benchmark for recognizing social relations, which learns high level semantic information of spatial, temporal, and audio of people’s social interactions in videos. Finally, we fuse them with logical regression to achieve accurate recognition. Experimental results show that the multi-stream deep model is effective for social relation recognition on the proposed dataset.

Multi-stream Fusion Model for Social Relation Recognition from Videos

Local Community Detection Based on Network Motifs

The automatic extraction of social relationship among individuals from massive quantities of video data is an important aspect of information extraction. However, most existing studies have focused on rough information, which result in inaccurate social network of role relationship. In this paper, the StoryRoleNet model is proposed for constructing an accurate and integral network representing the relationships among roles. First, to avoid the redundancy calculation of the relationships on the segmentation points of neighboring story units, we measure the weights of relationships by a weighted-Gaussian method in each story unit. More importantly, a new story segmentation method for long video is proposed by analyzing hierarchical features of the video. Then, we combine relationship networks constructed from the video and subtitle text. Some missed relationships can be complemented by this way. At last, the final network is analyzed to discover communities and important roles. Comprehensive evaluations were conducted using three movies and one television drama. The results demonstrate that the proposed method outperforms state-of-the-art methods in terms of the $F_{1}$ accuracy measure and the normalized mutual information value.

StoryRoleNet: Social Network Construction of Role Relationship in Video

Social networks that are dynamic contain rich network structure and content information. In dynamic networks, it is necessary to discover communities and their topical meanings. However, existing methods either only discover communities with ignoring their topical meaning in dynamic networks, or they discover communities and their topics in static networks. In this paper, we identify the problem of dynamic topical community detection and propose a dynamic topical community detection (DTCD) method to detect communities and their topical meanings in dynamic networks. The DTCD is a generative model integrating network structure, text, and time. The DTCD considers a community as a mixture of topics and generates the neighbors and documents of the node and their time stamps at the same time via the community. The latent variables are learned by collapsed Gibbs sampling. The DTCD not only can find communities and their topics, but also capture the temporal variations of communities and topics. The experimental results on two real-world datasets demonstrate the effectiveness of DTCD.

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Jinna Lv

Papers

Multi-feature Fusion for Predicting Social Media Popularity

Multi-stream Fusion Model for Social Relation Recognition from Videos

Local Community Detection Based on Network Motifs

StoryRoleNet: Social Network Construction of Role Relationship in Video

Dynamic Topical Community Detection in Social Network: A Generative Model Approach