[PDF][PDF] How noisy social media text, how diffrnt social media sources?

T Baldwin, P Cook, M Lui, A MacKinlay… - Proceedings of the sixth …, 2013‏ - aclanthology.org
While various claims have been made about text in social media text being noisy, there has
never been a systematic study to investigate just how linguistically noisy or otherwise it is …

Disturbed YouTube for kids: Characterizing and detecting inappropriate videos targeting young children

K Papadamou, A Papasavva, S Zannettou… - Proceedings of the …, 2020‏ - aaai.org
A large number of the most-subscribed YouTube channels target children of very young age.
Hundreds of toddler-oriented channels on YouTube feature inoffensive, well produced, and …

Online social network analysis: A survey of research applications in computer science

DB Kurka, A Godoy, FJ Von Zuben - arxiv preprint arxiv:1504.05655, 2015‏ - arxiv.org
The emergence and popularization of online social networks suddenly made available a
large amount of data from social organization, interaction and human behavior. All this …

[PDF][PDF] Social spammer detection in microblogging.

X Hu, J Tang, Y Zhang, H Liu - IJCAI, 2013‏ - labs.engineering.asu.edu
The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for
spammers to unfairly overpower normal users with unwanted content via social networks …

Analyzing disinformation and crowd manipulation tactics on YouTube

MN Hussain, S Tokdemir, N Agarwal… - 2018 IEEE/ACM …, 2018‏ - ieeexplore.ieee.org
YouTube, since its inception in 2005, has grown to become largest online video sharing
website. It's massive userbase uploads videos and generates discussion by commenting on …

N-gram assisted youtube spam comment detection

S Aiyar, NP Shetty - Procedia computer science, 2018‏ - Elsevier
This paper proposes a novel methodology for the detection of intrusive comments or spam
on the video-sharing website-Youtube. We describe spam comments as those which have a …

Semantic analysis on social networks: A survey

S Bayrakdar, I Yucedag, M Simsek… - International Journal of …, 2020‏ - Wiley Online Library
As social networks are getting more and more popular day by day, large numbers of users
becoming constantly active social network users. In this way, there is a huge amount of data …

Reverse nearest neighbors in large graphs

ML Yiu, D Papadias, N Mamoulis… - IEEE Transactions on …, 2006‏ - ieeexplore.ieee.org
A reverse nearest neighbor (RNN) query returns the data objects that have a query point as
their nearest neighbor (NN). Although such queries have been studied quite extensively in …

Commit: A scalable approach to mining communication motifs from dynamic networks

S Gurukar, S Ranu, B Ravindran - Proceedings of the 2015 ACM …, 2015‏ - dl.acm.org
A fundamental problem in behavioral analysis of human interactions is to understand how
communications unfold. In this paper, we study this problem by mining Communication …

Constrained NMF-based semi-supervised learning for social media spammer detection

D Yu, N Chen, F Jiang, B Fu, A Qin - Knowledge-Based Systems, 2017‏ - Elsevier
Within the past few years, social media platforms such as Facebook, Twitter, and Sina
Weibo, have gradually become important channels for information dissemination and …