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Broad Learning Introduction [chapter]

Jiawei Zhang, Philip S. Yu
2019 Broad Learning Through Fusions  
Discovering the social groups formed by users in online social networks is named as the community detection problem, and correctly detected social communities can be important for many social network services  ...  Based on the fused social networks, more information about the users can be collected, which can be used for more effective representation feature vector learning for the users.  ... 
doi:10.1007/978-3-030-12528-8_1 fatcat:dkhbs5k3o5co3ou6bde47pkqpu

"360° user profiling: past, future, and applications" by Aleksandr Farseev, Mohammad Akbari, Ivan Samborskii and Tat-Seng Chua with Martin Vesely as coordinator

Aleksandr Farseev, Mohammad Akbari, Ivan Samborskii, Tat-Seng Chua
2016 ACM SIGWEB Newsletter  
Users in social networks are often encouraged to complete their profile by providing personal attributes such as age, gender, interest, income, etc.  ...  In this paper, we discuss different user profiling approaches on social networks, highlight the challenges, techniques, and future trends.  ...  The Microsoft Windows Azure Cloud and Microsoft MSDN subscription were provided by "MB-Guide" 10 and "bBridge" 11 projects, as part of Microsoft BizSpark program.  ... 
doi:10.1145/2956573.2956577 fatcat:l6eaj76gvnahdkw7wlrhmtiohi

bBridge

Aleksandr Farseev, Ivan Samborskii, Tat-Seng Chua
2016 Proceedings of the 2016 ACM on Multimedia Conference - MM '16  
The system executes a community detection approach that considers the ability of social networks to complement each other during the process of latent representation learning, while the community profiling  ...  In this technical demonstration, we propose a cloud-based Big Data Platform for Social Multimedia Analytics called bBridge [9] that automatically detects and profiles meaningful user communities in a specified  ...  We are currently witnessing an explosive growth in social networking services, where users are publishing and consuming online contents.  ... 
doi:10.1145/2964284.2973836 dblp:conf/mm/FarseevSC16 fatcat:qp5fusg3tnhgdat4llmiu5lq3a

Online User Profiling to Detect Social Bots on Twitter [article]

Maryam Heidari, James H Jr Jones, Ozlem Uzuner
2022 arXiv   pre-print
Social bots are one of the significant sources of disinformation in social media. Social bots can pose serious cyber threats to society and public opinion.  ...  The new proposed model for bot detection creates user profiles based on personal information such as age, personality, gender, education from users' online posts and introduces a machine learning model  ...  Social media platforms can be a rich source of user content, and user's personal information can be disclosed in online platforms [4] and can be misused by social bots or Online fake identities to pose  ... 
arXiv:2203.05966v1 fatcat:wyzrsyaenfbjznjzhvl5xvgroa

Mental Health Computing via Harvesting Social Media Data

Jia Jia
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Initializing with binary user-level detection, we expand our research towards multiple contexts, by considering the trigger and level of mental health problems, and involving different social media platforms  ...  With the rapid development of social media, people are increasingly sharing their daily lives and interacting with friends online.  ...  Acknowledgments The author thanks Tat-Seng Chua of National University of Singapore and Wendy Hall of University of Southampton for their valuable instructions.  ... 
doi:10.24963/ijcai.2018/808 dblp:conf/ijcai/Jia18 fatcat:jk3pemvn7ba5jh2ofed2rw3jge

Mining Streaming and Temporal Data: from Representation to Knowledge

Xiangliang Zhang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this big-data era, vast amount of continuously arriving data can be found in various fields, such as sensor networks, network management, web and financial applications.  ...  representation learning.  ...  First, learning user representation comprehensively from their social activities by integrating multi-source information like user trajectories, tweets and social links.  ... 
doi:10.24963/ijcai.2018/821 dblp:conf/ijcai/Zhang18 fatcat:nygi5fmzm5gnbe6o7sovz3e6nm

ENWalk: Learning Network Features for Spam Detection in Twitter [chapter]

K. C. Santosh, Suman Kalyan Maity, Arjun Mukherjee
2017 Lecture Notes in Computer Science  
We propose ENWalk, a framework to detect the spammers by learning the feature representations of the users in the social media.  ...  We learn the feature representations using the random walks biased on the spam dynamics.  ...  Acknowledgements: This work is supported in part by NSF 1527364. We also thank anonymous reviewers for their helpful feedbacks.  ... 
doi:10.1007/978-3-319-60240-0_11 fatcat:3gz7dsbwm5ewjppugagletkxm4

Studying Fake News via Network Analysis: Detection and Mitigation [article]

Kai Shu, H. Russell Bernard, Huan Liu
2018 arXiv   pre-print
In this chapter, we will review network properties for studying fake news, introduce popular network types and how these networks can be used to detect and mitigation fake news on social media.  ...  However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information.  ...  Acknowledgements This material is based upon work supported by, or in part by, the ONR grant N ---, N , and N ---.  ... 
arXiv:1804.10233v1 fatcat:ll2cbfv3tbe7zclik34rrlofem

Towards Deep Learning Prospects: Insights for Social Media Analytics

Malik Khizar Hayat, Ali Daud, Abdulrahman A. Alshdadi, Ameen Banjar, Rabeeh Ayaz Abbasi, Yukun Bao, Hussain Dawood
2019 IEEE Access  
INDEX TERMS Social media data, dynamic network, deep learning, feature learning. 36958 He has published about 70 papers in reputed international Impact Factor journals and conferences.  ...  Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing.  ...  The data sources used as a ground truth are Quora, About.me, and LinkedIn. Social networks are the source of creating online relationships among users.  ... 
doi:10.1109/access.2019.2905101 fatcat:65mxyey3frdrfngvbfnfss3gpa

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications [article]

Shaoxiong Ji and Shirui Pan and Xue Li and Erik Cambria and Guodong Long and Zi Huang
2020 arXiv   pre-print
engineering or deep learning for automatic detection based on online social contents.  ...  Domain-specific applications of suicidal ideation detection are also reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content.  ...  In social networking services, posts with suicidal ideation are in the long tail of the distribution of different post categories.  ... 
arXiv:1910.12611v2 fatcat:63z4uvh5zrgyzb2bawtlbuo34m

A Comparison of Classical Versus Deep Learning Techniques for Abusive Content Detection on Social Media Sites [chapter]

Hao Chen, Susan McKeever, Sarah Jane Delany
2018 Lecture Notes in Computer Science  
The automated detection of abusive content on social media websites faces a variety of challenges including imbalanced training sets, the identification of an appropriate feature representation and the  ...  Classifiers such as support vector machines (SVM), combined with bag of words or ngram feature representation, have traditionally dominated in text classification for decades.  ...  Introduction An increasing number of social media platforms facilitate users in posting their personal opinions online, resulting in rapid growth in the volume of usergenerated content (UGC) over the past  ... 
doi:10.1007/978-3-030-01129-1_8 fatcat:yfxckqwom5f6rnenka5doemnpi

Knowledge Transferring via Model Aggregation for Online Social Care [article]

Shaoxiong Ji and Guodong Long and Shirui Pan and Tianqing Zhu and Jing Jiang and Sen Wang and Xue Li
2019 arXiv   pre-print
In particular, to evaluate the effectiveness of the learning algorithm, we use a case study on the early detection and prevention of suicidal ideation, and the experiment results on four datasets derived  ...  from social communities demonstrate the effectiveness of the proposed learning method.  ...  Shuai et al. used a machine learning based model to perform multi-source learning for mental disorder detection in social media [21] .  ... 
arXiv:1905.07665v2 fatcat:eas3zzmmznaslmwlhhzxwwycry

A System for Visualization and Analysis of Online Pedagogical Interactions

André Rei, Álvaro Figueira, Luciana Oliveira
2017 Proceedings of the 2017 International Conference on E-Education, E-Business and E-Technology - ICEBT 2017  
By defining these relationships between pairs of entities in an online learning environment (Moodle, in our case) our tool creates a graph, where it is possible to apply techniques of social network analysis  ...  We present a system for a dynamic graphical representation of the interactions captured in educational online environments.  ...  As a consequence, we address the detected problem, and a system for visualizing and analyzing online social interactions, integrated in an LMS, is proposed.  ... 
doi:10.1145/3141151.3141161 fatcat:hwd34bowhbauxneef4uay22fzq

From Retweet to Believability

Bhavtosh Rath, Wei Gao, Jing Ma, Jaideep Srivastava
2017 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 - ASONAM '17  
Ubiquitous use of social media such as microblogging platforms brings about ample opportunities for the false information to diffuse online.  ...  With the retweet network edge-weighted by believability scores, we use network representation learning to generate user embeddings, which are then leveraged to classify users into as rumor spreaders or  ...  strengthens the representation of user properties in consideration of information veracity using network feature learning based on a large-scale believability re-weighted trust network.  ... 
doi:10.1145/3110025.3110121 dblp:conf/asunam/RathGMS17 fatcat:4cuoflyjgffjtkf7xsxxpbbalu

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media [article]

Yi-Ju Lu, Cheng-Te Li
2020 arXiv   pre-print
This paper solves the fake news detection problem under a more realistic scenario on social media.  ...  Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting  ...  Acknowledgments This work is supported by Ministry of Science and Technology (MOST) of Taiwan under grants 109-2636-E-006-017 (MOST Young Scholar Fellowship) and 108-2218-E-006-036, and also by Academia  ... 
arXiv:2004.11648v1 fatcat:fwzwjwqggbff3c5q4nvzh5lalm
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