Deep Attentive Multimodal Network Representation Learning for Social Media Images

2021 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Feiran Huang ◽  
Chaozhuo Li ◽  
Boyu Gao ◽  
Yun Liu ◽  
Sattam Alotaibi ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Large Scale ◽  
Smart Cities ◽  
Representation Learning ◽  
Media Images ◽  
Social Image ◽  
Comprehensive Information ◽  
Intelligent Information ◽  
Low Dimensional

The analysis for social networks, such as the socially connected Internet of Things, has shown a deep influence of intelligent information processing technology on industrial systems for Smart Cities. The goal of social media representation learning is to learn dense, low-dimensional, and continuous representations for multimodal data within social networks, facilitating many real-world applications. Since social media images are usually accompanied by rich metadata (e.g., textual descriptions, tags, groups, and submitted users), simply modeling the image is not effective to learn the comprehensive information from social media images. In this work, we treat the image and its textual description as multimodal content, and transform other metainformation into the links between contents (such as two images marked by the same tag or submitted by the same user). Based on the multimodal content and social links, we propose a Deep Attentive Multimodal Graph Embedding model named DAMGE for more effective social image representation learning. We introduce both small- and large-scale datasets to conduct extensive experiments, of which the results confirm the superiority of the proposal on the tasks of social image classification and link prediction.

scholarly journals Social Media-based User Embedding: A Literature Review

2019 ◽  
Author(s):  
Shimei Pan ◽  
Tao Ding
Keyword(s):  
Social Media ◽  
High Performance ◽  
Large Scale ◽  
Ground Truth ◽  
Representation Learning ◽  
Success Stories ◽  
Recent Success ◽  
User Data ◽  
Low Dimensional ◽  
And Behavior

Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g., combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions.

Crowdsourcing Incident Information for Emergency Response using Open Data Sources in Smart Cities

2018 ◽  
Vol 2672 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Fan Zuo ◽  
Abdullah Kurkcu ◽  
Kaan Ozbay ◽  
Jingqin Gao
Keyword(s):  
Social Media ◽  
Prior Knowledge ◽  
Emergency Response ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Smart Cities ◽  
Hurricane Sandy ◽  
Support Vector ◽  
Twitter Data ◽  
Emergency Events

Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.

scholarly journals RE-ENGINEERING OF SMART CITY'S BUSINESS PROCESSES BASED ON SOCIAL NETWORKS AND INTERNET OF THINGS

2018 ◽  
Vol 16 (3) ◽  
pp. 275
Author(s):  
Emir Ugljanin ◽  
Dragan Stojanović ◽  
Ejub Kajan ◽  
Zakaria Maamar
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Internet Of Things ◽  
Smart City ◽  
Business Processes ◽  
Smart Cities ◽  
Successful Management ◽  
Complete Integration ◽  
Social Actions

This paper reports our experience with developing a Business-2-Social (B2S) platform that provides necessary support to all this platform’s constituents, namely business processes, social media (e.g., social network), and Internet of Things (IoT). This platform is exemplified with smart cities whose successful management requires a complete integration of IoT and social media capabilities into the business processes implementing user services. To ensure a successful integration, social actions, that a smart city would allow citizens execute, are analyzed in terms of impact of these smart city’s business processes. Reactions to these actions are tracked and then analyzed to improve user services.

scholarly journals Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7115
Author(s):  
Amin Muhammad Sadiq ◽  
Huynsik Ahn ◽  
Young Bok Choi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Large Scale ◽  
Activity Analysis ◽  
Physical Activities ◽  
Future Research ◽  
Crowd Sourcing ◽  
Emergency Relief ◽  
The Public ◽  
Text Streams

A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in sentiment and activity analysis. Although sentiment and activity analysis of text streams has been extensively studied in the literature, it is relatively recent yet challenging to evaluate sentiment and physical activities together from visuals such as photographs and videos. This paper emphasizes human sentiment in a socially crucial field, namely social media disaster/catastrophe analysis, with associated physical activity analysis. We suggest multi-tagging sentiment and associated activity analyzer fused with a a deep human count tracker, a pragmatic technique for multiple object tracking, and count in occluded circumstances with a reduced number of identity switches in disaster-related videos and images. A crowd-sourcing study has been conducted to analyze and annotate human activity and sentiments towards natural disasters and related images in social networks. The crowdsourcing study outcome into a large-scale benchmark dataset with three annotations sets each resolves distinct tasks. The presented analysis and dataset will anchor a baseline for future research in the domain. We believe that the proposed system will contribute to more viable communities by benefiting different stakeholders, such as news broadcasters, emergency relief organizations, and the public in general.

A Perspective of Evolution After Five Years: A Large-Scale Study of Web Spam Evolution

2014 ◽  
Vol 23 (02) ◽  
pp. 1441001 ◽  
Author(s):  
De Wang ◽  
Danesh Irani ◽  
Calton Pu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Large Scale ◽  
Web Pages ◽  
Spam Filtering ◽  
Web Page ◽  
New Techniques ◽  
Web Spam ◽  
Classification Evaluation ◽  
Over Time

Identifying and detecting web spam is an ongoing battle between spam-researchers and spammers which has been going on since search engines allowed searching of web pages to the modern sharing of web links via social networks. A common challenge faced by spam-researchers is the fact that new techniques depend on requiring a corpus of legitimate and spam web pages. Although large corpora of legitimate web pages are available to researchers, the same cannot be said about web spam or spam web pages. In this paper, we introduce the Webb Spam Corpus 2011 — a corpus of approximately 330,000 spam web pages — which we make available to researchers in the fight against spam. By having a standard corpus available, researchers can collaborate better on developing and reporting results of spam filtering techniques. The corpus contains web pages crawled from links found in over 6.3 million spam emails. We analyze multiple aspects of this corpus including redirection, HTTP headers, web page content, and classification evaluation. We also provide insights into changes in web spam since the last Webb Spam Corpus was released in 2006. These insights include: (1) spammers manipulate social media in spreading spam; (2) HTTP headers and content also change over time; (3) spammers have evolved and adopted new techniques to avoid the detection based on HTTP header information.

scholarly journals Finding Potential Propagators and Customers in Location-Based Social Networks: An Embedding-Based Approach

2020 ◽  
Vol 10 (22) ◽  
pp. 8003
Author(s):  
Yi-Chun Chen ◽  
Cheng-Te Li
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Main Idea ◽  
Feature Representation ◽  
Specific Point ◽  
Point Of Interest ◽  
The Future ◽  
Location Based Social Networks ◽  
Low Dimensional ◽  
Embedding Methods

In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI). While existing studies mainly focus on accurately recommending POIs for users, less effort is made for identifying propagators in LBSN. In this work, we propose and tackle two novel tasks, Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD), in the context of Location Promotion. Given a target POI l to be promoted, TPD aims at finding a set of influential users, who can generate more users to visit l in the future, and TCD is to find a set of potential users, who will visit l in the future. To deal with TPD and TCD, we propose a novel graph embedding method, LBSN2vec. The main idea is to jointly learn a low dimensional feature representation for each user and each location in an LBSN. Equipped with learned embedding vectors, we propose two similarity-based measures, Influential and Visiting scores, to find potential targeted propagators and customers. Experiments conducted on a large-scale Instagram LBSN dataset exhibit that LBSN2vec and its variant can significantly outperform well-known network embedding methods in both tasks.

scholarly journals Social Media Use Predicts Greater Liking in In-Person Initial Interactions

2021 ◽  
pp. 014616722110409
Author(s):  
Jennifer L. Heyman ◽  
Lauren Gazzard Kerr ◽  
Lauren J. Human
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Interactions ◽  
Online Social Networks ◽  
Large Scale ◽  
Media Use ◽  
First Impressions ◽  
Social Media Use ◽  
Large Scale Study ◽  
Interaction Partners

Does how people generally engage with their online social networks relate to offline initial social interactions? Using a large-scale study of first impressions ( N = 806, Ndyad = 4,565), we examined how different indicators of social media use relate to the positivity of dyadic in-person first impressions, from the perspective of the participants and their interaction partners. Many forms of social media use (e.g., Instagram, Snapchat, passive) were associated with liking and being liked by others more, although some forms of use (e.g., Facebook, active) were not associated with liking others or being liked by others. Furthermore, most associations held controlling for extraversion and narcissism. Thus, while some social media use may be generally beneficial for offline social interactions, some may be unrelated, highlighting the idea that how, rather than how much, people use social media can play a role in their offline social interactions.

scholarly journals Social behavior prediction with graph U-Net+

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyue Yan ◽  
Wenming Cao ◽  
Jianhua Ji
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Feature Extraction ◽  
Binary Classification ◽  
Representation Learning ◽  
Graph Representation ◽  
Behavior Prediction ◽  
Graph Node ◽  
Future Behavior ◽  
Decoder Architecture

AbstractWe focus on the problem of predicting social media user’s future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. We propose an encoder–decoder architecture based on graph U-Net, known as the graph U-Net+. In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization approaches into both encoding and decoding blocks. We reuse four datasets from DeepInf and extensive experimental results demonstrate that our methods achieve better performance than previous models.

scholarly journals DLDTI: A learning-based framework for identification of drug-target interaction using neural networks and network representation

2020 ◽  
Author(s):  
Yihan Zhao ◽  
Kai Zheng ◽  
Baoyi Guan ◽  
Mengmeng Guo ◽  
Lei Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Drug Target ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Representation Learning ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Mapping Space ◽  
Network Representation ◽  
Low Dimensional

AbstractTo elucidate novel molecular mechanisms of known drugs, efficient and feasible computational methods for predicting potential drug-target interactions (DTI) would be of great importance. A novel calculation model called DLDTI was generated for predicting DTI based on network representation learning and convolutional neural networks. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning low-dimensional and rich depth features of drugs and proteins. Low-dimensional feature vectors were used to train DLDTI to obtain optimal mapping space and infer new DTIs by ranking DTI candidates based on their proximity to optimal mapping space. DLDTI achieves promising performance under 5-fold cross-validation with AUC values of 0.9172, which was higher than that of the method based on different classifiers or different feature combination technique. Moreover, biomedical experiments were also completed to validate DLDTI’s performance. Consistent with the predicted result, tetramethylpyrazine, a member of pyrazines, reduced atherosclerosis progression and inhibited signal transduction in platelets, via PI3K/Akt, cAMP and calcium signaling pathways. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI

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