A short-term trend prediction model of topic over Sina Weibo dataset

Zhao, Juanjuan; Wu, Weili; Zhang, Xiao Long; Qiang, Yan; Liu, Tao; Wu, Lidong

doi:10.1007/s10878-013-9674-0

Cited by 13 publications

(10 citation statements)

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“…To understand the context into which 50c posts are inserted, we began by estimating the total number of Chinese social media posts nationwide. As of December 2012, netizens were posting approximately 100 million messages a day, or 36.5 billion a year, on Sina Weibo alone (Zhao et al 2014), which is one of at least 1,382 known social media sites (King et al 2013). In our data, the ratio of Sina Weibo posts to all posts is 1.85, meaning that an estimate of the total number of posts on all platforms is (1.85 × 36.5 billion =) 67.5 billion.…”

Section: Size Of the 50c Partymentioning

confidence: 99%

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument

2017

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INTRODUCTION Social media in China appears as vibrant and extensive as in any Western country, with more than 1,300 social media companies and websites, and millions of posts authored every day by people all over the country. At the same time, the Chinese regime imposes extensive and varied controls over of the entire system (Brady 2009;Cairns and Carlson 2016;Knockel et al. 2015;MacKinnon 2012;Ng 2015;Shirk 2011;Stockmann 2013;Stockmann and Gallagher 2011;Yang 2009). Which social media companies are prevented from operating in China is easy to see (the so-called Great Firewall of China), and the scholarly literature now offers considerable evidence on how and why Gary King is Albert J. Weatherhead III University Professor, Institute for Quantitative Social Science, 1737 Cambridge St., Harvard University, Cambridge MA 02138; GaryKing.org (King@Harvard.edu).Jennifer Pan is Assistant Professor, Department of Communication, 450 Serra Mall, Building 120, Stanford University, Stanford CA 94304; jenpan.com (jp1@stanford.edu).Margaret E. Roberts is Assistant Professor, Department of Political Science, University of California, San Diego, Social Sciences Building 301, 9500 Gilman Dr., #0521, La Jolla, CA 92093-0521; MargaretRoberts.net (meroberts@ucsd.edu).Our thanks to Neel Guha, Peter Dyrud, Yingjie Fan, and many others for superb research assistance; Danielle Allen, Peter Bol, Becky Fair, Chase Harrison, Iain Johnston, Franziska Keller, Blake Miller, Jean Oi, Samantha Ravich, Brandon Stewart, Daniela Stockmann, Andy Walder, Yuhua Wang, Chaodan Zheng, and Yun Zhu for helpful comments; and DARPA (contract W31P4Q-13-C-0055/983-3) and the National Science Foundation (grant 1500086) for research support. Authors are listed alphabetically. Data and information necessary to replicate the results in this article appear in King et al. (2017).Received: August 26, 2016; revised: November 6, 2016; accepted: April 11, 2017. they censor certain individual social media posts that have appeared on the web or filter them out before appearing. In both cases, the censorship apparatus allows a great deal of criticism of the regime, its officials, and their policies (which can be useful information for the central government in managing local leaders) but stops discussions that can generate collective action on the ground . 1According to numerous speculations by scholars, activists, journalists, officials in other governments, and participants in social media, the Chinese regime also conducts "astroturfing," or what we might call reverse censorship, surreptitiously posting large numbers of fabricated social media comments as if they were the genuine opinions of ordinary Chinese people. The people hired for this purpose are known formally as Internet commentators ( ), although more widely as 50c party members ( ), so called because they are rumored to be paid 50 cents (5 jiao, , or about $0.08) to write and post each comment (Tong and Lei 2013). We show that this rumor turns out to be incorrect; however, we adopt this widely used ter...

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Section: Size Of the 50c Partymentioning

confidence: 99%

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument

2017

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“…As part of a social interaction, sharing a check-in allows users to announce the places they visit (e.g., restaurants, shopping malls, and popular scenic areas). This check-in phenomenon generates an enormous amount of user data (also referred "Big Data" [14]) and has attracted more than 222 million subscribers; statistics showed there were 500 million users with more than 100 million daily users on Weibo by the third quarter of 2015 [15][16][17]. Regardless of some limitations on representing check-in behavior, e.g., the bias of gender, a low sampling frequency, and the bias of location category, check-in data can uncover check-in behavior within a city.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Muhammad

Wan

2018

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With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.

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“…Liu et al ( 2015 ) used the frequency accumulation of topics in different periods to predict whether a topic would be popular. Zhao et al ( 2014 ) proposed a method to predict short-term topic trends by calculating the “growth factor” of a topic, and considered that the increasing speed of the number of topic documents affects the “growth factor”. However, a document may contain multiple topics, and the contribution of the same feature words to different topics is different, so the contribution of feature words to a topic should be considered in topic intensity evolution.…”

Section: Related Workmentioning

confidence: 99%

Evolution analysis of online topics based on ‘word-topic’ coupling network

Zhu

Qin

et al. 2022

Scientometrics

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Analyzing topic evolution is an effective way to monitor the overview of topic spreading. Existing methods have focused either on the intensity evolution of topics along a timeline or the topic evolution path of technical literature. In this paper, we aim to study topic evolution from a micro perspective, which not only captures the topic timeline but also reveals the topic status and the directed evolutionary path among topics. Firstly, we construct a word network by co-occurrence relationship between feature words. Secondly, Latent Dirichlet allocation (LDA) model is used to automatically extract topics and capture the mapping relationship between words and topics, and then a ‘word-topic’ coupling network is built. Thirdly, based on the ‘word-topic’ coupling network, we describe the topic intensity evolution over time and measure topic status considering the contribution of feature words to a topic. The concept of topic drifting probability is proposed to identify the evolutionary path. Experimental results conducted on two real-world data sets of “COVID-19” demonstrate the effectiveness of our proposed method.

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A short-term trend prediction model of topic over Sina Weibo dataset

Cited by 13 publications

References 10 publications

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Evolution analysis of online topics based on ‘word-topic’ coupling network

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