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Measuring User Influence in Twitter: The Million Follower Fallacy
An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
On the evolution of user interaction in Facebook
It is found that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages.
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
This paper analyzed YouTube, the world's largest UGC VoD system, and provided an in-depth study of the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system.
Detecting Rumors from Microblogs with Recurrent Neural Networks
A novel method that learns continuous representations of microblog events for identifying rumors based on recurrent neural networks that detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.
A measurement-driven analysis of information propagation in the flickr social network
Analysis of large-scale traces of information dissemination in the Flickr social network finds that even popular photos do not spread widely throughout the network, and the role of word-of-mouth exchanges between friends in the overall propagation of information in the network is questioned.
Prominent Features of Rumor Propagation in Online Social Media
- Sejeong Kwon, M. Cha, Kyomin Jung, Wei Chen, Yajun Wang
- Computer ScienceIEEE 13th International Conference on Data Mining
- 1 December 2013
A new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have fluctuations over time, and key structural and linguistic differences in the spread of rumors and non-rumors are identified.
Characterizing user behavior in online social networks
- Fabrício Benevenuto, Tiago Rodrigues, M. Cha, Virgílio A. F. Almeida
- Computer ScienceIMC '09
- 4 November 2009
A first of a kind analysis of user workloads in online social networks, based on detailed clickstream data collected over a 12-day period, shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities.
Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems
- M. Cha, Haewoon Kwak, P. Rodriguez, Yong-Yeol Ahn, S. Moon
- Computer ScienceIEEE/ACM Transactions on Networking
- 1 October 2009
This paper empirically shows how UGC services are fundamentally different from traditional VoD services, and analyzes the intrinsic statistical properties of UGC popularity distributions, which discuss opportunities to leverage the latent demand for niche videos (or the so-called "the Long Tail" potential).
Watching television over an IP network
This paper presents the first analysis of IPTV workloads based on network traces from one of the world's largest IPTV systems, and describes the properties of viewing sessions, channel popularity dynamics, geographical locality, and channel switching behaviors.
Comparing and combining sentiment analysis methods
A new method that combines existing approaches, providing the best coverage results and competitive agreement is developed and a free Web service called iFeel is presented, which provides an open API for accessing and comparing results across different sentiment methods for a given text.