Twitterrank: Finding topic-sensitive influential Twitterers

This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called “following”, in which each user can choose who she wants to “follow ” to receive tweets from without requir-ing the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4 % of the users in Twitter follow more than 80 % of their followers, and (2) 80.5 % of the users have 80 % of users they are following follow them back. Our study reveals that the presence of “reciprocity ” can be explained by phenomenon of homophily [14]. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence takin

SONAR: Automatic detection of cyber security events over the twitter stream

© 2017 ACM. Everyday, security- experts face a grim ing number of security events that affecting people well-being, their information systems and sometimes the critical infrastructure. The sooner they can detect and understand these threats, the more they can mitigate and forensically investigate them Therefore, they need to have a situation awareness of the existing security events and their possible effects. However, given the large number of events, it can be difficult for security analysts and researchers to handle this flow of information in an adequate manner and answer the following questions in near- real time: what are the current security events? How long do they last? In this paper, we will try to answer these issues by leveraging social networks that contain a massive amount of valuable information on many topics. I lowever. because of the very- high volume, extracting meaningful information can be challenging. For this reason, we propose SONAR: An automatic, self-learned framework that can detect geolocate and categorize cyber security events in near-real time over the Twitter stream. SONAR is based on a taxonomy- of cyber security events and a set of seed keywords describing type of events that we want to follow in order to start detecting events. Using these seed keywords, it automatically discovers new relevant keywords such as malware names to enhance the range of detection while staying in the same domain. Using a custom taxonomy describing all type of cyber threats, we demonstrate the capabilities of SONAR on a dataset of approximately 47.8 million tweets related to cyber security in the last 9 months. SONAR could efficiently and effectively detect, categorize and monitor cyber security related events before getting on the security news, and it could automatically discover new security terminologies with their event. Additionally. SONAR is highly scalable and customizable by design; therefore we could adapt SONAR framework for virtually any type of events that experts are interested in

Mining interesting link formation rules in social networks

Link structures are important patterns one looks out for when modeling and analyzing social networks. In this pa-per, we propose the task of mining interesting Link For-mation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns cap-ture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of exist-ing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to us-ing a support-confidence framework for measuring the fre-quency and significance of LF-rules, we introduce the notion of expected support to account for the extent to which LF-rules exist in a social network by chance. Specifically, only LF-rules with higher-than-expected support are considered interesting. We conduct empirical studies on two real-world social networks, namely Epinions and myGamma. We re-port interesting LF-rules mined from the two networks, and compare our findings with earlier findings in social network analysis

Comparing Twitter and Traditional Media using Topic Models

NSF

Event Detection in Twitter

Twitter, as a form of social media, is fast emerging in recent years. Users are using Twitter to report real-life events. This paper focuses on  detecting those events by analyzing the text stream in Twitter. Although event detection has long been a research topic, the characteristics of Twitter make it a non-trivial task. Tweets reporting such events are usually overwhelmed by high flood of meaningless “babbles”. Moreover, event detection algorithm needs to be scalable given the sheer amount of tweets. This paper attempts to tackle these challenges with EDCoW (Event Detection with Clustering of Wavelet-based Signals). EDCoW builds signals for individual words by applying wavelet analysis on the frequencybased raw signals of the words. It then filters away the trivial words by looking at their corresponding signal autocorrelations. The remaining words are then clustered to form events with a modularity-based graph partitioning technique. Experimental results show promising result of EDCoW

Federated Learning for Electronic Health Records

10.1145/3514500ACM Transactions on Intelligent Systems and Technolog