PPT-Community Detection in Political Twitter Networks using Non

Mert Ozer Nyunsu Kim Hasan Davulcu Motivation Motivation Motivation Motivation Group Them Group Them Motivation Jointly clustering connectivity and content of tweeters to detect latent . Liu. , . Ya. Tian. , . Yujia. Pham, . Anh. TwitterMonitor. : . Trend Detection over the Twitter Stream. EvenTweet. : . Online Localized Event Detection from Twitter. Michael . Mathioudakis. , Nick . Koudas. Learning to . Sparsify. for Detection in Massive Noisy Sensor Networks. IPSN 4/9/2013. Matthew Faulkner. . Annie Liu. . Andreas Krause. Community Sensors. More than 1 Billion smart devices provide powerful internet-connected sensor packages.. inWireless. Sensor Networks. Abstract. Wireless sensor networks are vulnerable to the node clone, and several distributed protocols have been proposed to . de¬tect. this attack. . However, they require too strong assumptions to be practical for large-scale, randomly deployed sensor networks. . presented by . Mert Ozer. Politics & Social Media. Politics & Social Media. Politics & Social Media. Politics & Social Media. Politics & Social Media. Challenges. Availability of ground-truth for supervised models.. Application to voting patterns in the US Senate. Based on . “Sequential . detection of temporal communities . by . estrangement . confinement”. Kawadia. , Sreenivasan, . Sci. Rep. . 1. :794 (2015). Generally a DAG, directed acyclic graph. VisGraph, HKUST. LeNet. AlexNet. ZF Net. GoogLeNet. VGGNet. ResNet. Learned convolutional filters: Stage 1. Visualizing and understanding convolutional neural networks.. , . Yujia. Pham, . Anh. TwitterMonitor. : . Trend Detection over the Twitter Stream. EvenTweet. : . Online Localized Event Detection from Twitter. Michael . Mathioudakis. , Nick . Koudas. TwitterMoniter. Monterey, California. rgera@nps.edu. Overview of. Communities in networks. What is a community?. A community . ~ a group of people with common characteristic or shared interests. What do they correspond to?. Akemi Takeoka Chatfield, Christopher G. Reddick, Uuf Brajawidagda. Presented By: Abel Jose, . Adarsh. Pillay, Deepak . Begrajka,. Rudra Gupta . CONTENT. Background. Introduction. Framework. Research Methodology. By . Gianluca. . Stringhini. , . Christopher . Kruegel. and Giovanni . Vigna. Presented By Awrad Mohammed Ali. 1. Introduction.. The Popular Social Networks.. Data Collection.. Data Analysis.. Spam Bots Analysis.. Narisong Huhe and Mark Shephard. Importance. Young people are most likely to rely on social media for news:. . 37% use Facebook. . 22% use Twitter. (Digital News Report 2017). Not just elections, there is an increased use of referenda in UK (and often big constitutional issues e.g. 2014 + 2016). It’s no secret that this world we live in can be pretty stressful sometimes. If you find yourself feeling out-of-sorts, pick up a book.According to a recent study, reading can significantly reduce stress levels. In as little as six minutes, you can reduce your stress levels by 68%. A Behavioral Modeling . Approach. Ashwin. . Rajadesingan. , Reza Zafarani, and . Huan. . Liu. Sarcasm. . . a . nuanced form of language where usually, the user explicitly states the opposite of what she implies. . Article Review. Billy Spann. IFSC 7310 Spring 2019. Article Review. Reference Citation. The Impact of Twitter Adoption on Lawmakers’ Voting Orientations. Mousavi, Reza, and Bin Gu. “The Impact of Twitter Adoption on Lawmakers’ Voting Orientations.” .