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The World of Connections and Information Flow in Twitter
Meeyoung Cha,Benevenuto, F.,Haddadi, H.,Gummadi, K. IEEE 2012 IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS P Vol.42 No.4
<P>Information propagation in online social networks like Twitter is unique in that word-of-mouth propagation and traditional media sources coexist. We collect a large amount of data from Twitter to compare the relative roles different types of users play in information flow. Using empirical data on the spread of news about major international headlines as well as minor topics, we investigate the relative roles of three types of information spreaders: 1) mass media sources like BBC; 2) grassroots, consisting of ordinary users; and 3) evangelists, consisting of opinion leaders, politicians, celebrities, and local businesses. Mass media sources play a vital role in reaching the majority of the audience in any major topics. Evangelists, however, introduce both major and minor topics to audiences who are further away from the core of the network and would otherwise be unreachable. Grassroots users are relatively passive in helping spread the news, although they account for the 98% of the network. Our results bring insights into what contributes to rapid information propagation at different levels of topic popularity, which we believe are useful to the designers of social search and recommendation engines.</P>
Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems
Meeyoung Cha,Haewoon Kwak,Rodriguez, P.,Yong-Yeol Ahn,Sue Moon IEEE 2009 IEEE/ACM transactions on networking Vol.17 No.5
<P>User generated content (UGC), now with millions of video producers and consumers, is reshaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world's largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show how UGC services are fundamentally different from traditional VoD services. We then analyze the intrinsic statistical properties of UGC popularity distributions and discuss opportunities to leverage the latent demand for niche videos (or the so-called 'the Long Tail' potential), which is not reached today due to information filtering or other system scarcity distortions. Based on traces collected across multiple days, we study the popularity lifetime of UGC videos and the relationship between requests and video age. Finally, we measure the level of content aliasing and illegal content in the system and show the problems aliasing creates in ranking the video popularity accurately. The results presented in this paper are crucial to understanding UGC VoD systems and may have major commercial and technical implications for site administrators and content owners.</P>
Gabriel Lima,Meeyoung Cha,Chiyoung Cha,Hyeyoung Hwang 한국자료분석학회 2021 Journal of the Korean Data Analysis Society Vol.23 No.3
This study presents survey results of the public’s willingness to get vaccinated against COVID-19 during an early phase of the pandemic and examines factors that could influence vaccine acceptance based on a between-subjects design. A representative quota sample of 572 adults in the US and UK participated in an online survey. First, the participants’ medical use tendencies and initial vaccine acceptance were assessed; then, short vignettes were provided to evaluate their changes in attitude towards COVID-19 vaccines. For data analysis, ANOVA and post hoc pairwise comparisons were used. The participants were more reluctant to vaccinate their children than themselves and the elderly. The use of artificial intelligence (AI) in vaccine development did not influence vaccine acceptance. Vignettes that explicitly stated the high effectiveness of vaccines led to an increase in vaccine acceptance. Our study suggests public policies emphasizing the vaccine effectiveness against the virus could lead to higher vaccination rates. We also discuss the public’s expectations of governments concerning vaccine safety and present a series of implications based on our findings.
Jisun An,Meeyoung Cha,Krishna Gummadi,Jon Crowcroft 한국산업응용수학회 2011 한국산업응용수학회 학술대회 논문집 Vol.6 No.1
We present a preliminary but groundbreaking study of the media landscape of Twitter. We use public data on whom follows who to uncover common behavior in media consumption, the relationship between various classes of media, and the diversity of media content which social links may bring. Our analysis shows that there is a non-negligible amount of indirect media exposure, either through friends who follow particular media sources, or via retweeted messages. We show that the indirect media exposure expands the political diversity of news to which users are exposed to a surprising extent, increasing the range by between 60-98%. These results are valuable because they have not been readily available to traditional media, and they can help predict how we will read news, and how publishers will interact with us in the future.
박건우(Kunwoo Park),차미영(Meeyoung Cha) Korean Institute of Information Scientists and Eng 2017 정보과학회논문지 Vol.44 No.3
In MMORPG (Massively Multiplayer Online Role-Playing Game), users advance their own characters to get to the maximum (max) level by performing given tasks in the game scenario. Although it is crucial to retain users with high levels for running online games successfully, little efforts have been paid to investigate them. In this study, by analyzing approximately 60 million in-game logs of over 50,000 users, we aimed to investigate the process through which users achieve the max level and churn of such users since the moment of achieving the max level, and determine possible indicators related to churn after the max level. Based on the result, we can predict churn of the max level users by employing behavioral patterns before the max level. Moreover, we found users who are socially active and communicate with many people before the max level are less likely to leave the service (p<0.05). This study supports that communication patterns are important factors for persistent usage of the users who achieve the max level, which has practical implications to guide elite users on enjoying online games in the long run.
초기 소량 데이터와 RNN을 활용한 루머 전파 추적 기법
권세정(Sejeong Kwon),차미영(Meeyoung Cha) Korean Institute of Information Scientists and Eng 2017 정보과학회논문지 Vol.44 No.7
The emergence of online media and their data has enabled data-driven methods to solve challenging and complex tasks such as rumor classification problems. Recently, deep learning based models have been shown as one of the fastest and the most accurate algorithms to solve such problems. These new models, however, either rely on complete data or several days-worth of data, limiting their applicability in real time. In this study, we go beyond this limit and test the possibility of super early rumor detection via recurrent neural networks (RNNs). Our model takes in social media streams as time series input, along with basic meta-information about the rumongers including the follower count and the psycholinguistic traits of rumor content itself. Based on analyzing millions of social media posts on 498 real rumors and 494 non-rumor events, our RNN-based model detected rumors with only 30 initial posts (i.e., within a few hours of rumor circulation) with remarkable F1 score of 0.74. This finding widens the scope of new possibilities for building a fast and efficient rumor detection system.