Predicting the popularity growth of online content: Model and algorithm

Lymperopoulos, Ilias N.

doi:10.1016/j.ins.2016.07.043

Cited by 30 publications

(21 citation statements)

References 14 publications

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“…Online content popularity has an enormous impact on opinions, culture, policy, and profits, especially with the advent of Web 2.0 and social media. In last decade, quantitative understanding the popularity dynamics of online content has been attracting much attention from academia [1][2][3][4][5]. Popularity dynamics represents many real social phenomena, such as video views on YouTube [6][7][8][9][10][11][12][13], reading volume of tweets and news on social media [14][15][16][17][18][19], and movie views on online system [20][21][22].…”

Section: Modeling and Predicting Popularity Dynamicsmentioning

confidence: 99%

“…Data type includes online videos, blogs, micro-blogs, articles, news, hashtags, and more. The models and algorithms used are also very different, such as rank-shift model [1], SEISMIC algorithm [16], K-SC clustering algorithm [30], Popularity growth model [2], and so on. Our research and previous research are compared in Table 1:…”

Section: Research Summary and Comparisonmentioning

confidence: 99%

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Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

2019

J Big Data

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BackgroundWith the rapid development of mobile Internet technology, human society has entered an era of high interconnection between human and information. Especially the emergence of online social networks, such as Weibo in China, LinkedIn, Twitter, Face-book and so on, because of its high interaction and strong group participation, makes many related information converging and merging, leading to the emergence of hot topics on the Internet. These hot topics are the aggregation of information about current events (e.g., natural disasters, sports news, celebrity news), which have geotags-timestamp information. These metadata (geotags and timestamp) are embedded within the content of the message, so that the analysis of an event can be performed by applying a spacetime classification. In addition, the hot topic is the sprout of the network public opinion. Network public opinion has an important impact on the stability of the country and society. Therefore, the popularity of hot topics is studied from the spatial and temporal dimension can better understand the dissemination of public opinion. This paper uses the data of Sina Weibo, one of the largest micro-blogging system in China (https ://weibo .com/), to study the following two questions: (1) study the classification problem of hot topic on Weibo, cluster the popularity time series by clustering algorithm;(2) study the spatial distribution characteristics of hot topic on Weibo based on location label data. AbstractIn this paper, a quantitative temporal and spatial analysis of the dynamics of hot topics popularity in Micro-blogging system was provided. Firstly, the popularity time series of 1167 hot topics were counted and calculated by Excel. Secondly, based on MAT-LAB software,the popularity time series were clustered into six clusters by K-spectral centroid (K-SC) clustering algorithm. Thirdly, we analyzed temporal patterns and spatial patterns of popularity dynamics of topics by statistical methods. The results show that temporal popularity of micro-blogging topics is rapidly dying, and the distribution of popularity is subject to the power law form. In addition, most of the Micro-blogging topics are global topic. Our results can provide a literature reference for studying the influence of online hot topics and the evolution of public opinion. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Modeling and Predicting Popularity Dynamicsmentioning

confidence: 99%

Section: Research Summary and Comparisonmentioning

confidence: 99%

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

2019

J Big Data

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“…Numerous studies have been published evidencing the characteristics of the Millennials and the organizational response to deal with them [3]. Other authors argue that, as social networks become ubiquitous as the principal tool for the dissemination of information, forecasting the popularity of online content is increasingly important for the management of brand reputation, growth of business opportunities and effective communication [4].…”

Section: Introductionmentioning

confidence: 99%

Comparing the Consumption of Musical and Films Online between Generations X and Y in Portugal and Spain

Trindade

Duarte

2019

2019 14th Iberian Conference on Information Systems and Technologies (CISTI)

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Numerous studies have been published evidencing the characteristics of Millennial generation (also known as Gen Y) by comparison with generation X or Gen X. The internet affirms itself as a differentiating factor and determinant of attitudes and behaviors. Considering the ease of access and the familiarity of Millennials, with the new technologies, it lacks analysis the preferences of use and consumption of music and films and television series, anchored in the Internet. The present study seeks to understand if there are differences between the Portuguese and Spanish users' behavior or perceptions about the consumption of Internet contents in terms of music (or music videos) and films and television series and if such differences are reflected in the age groups. Using the Flash Eurobarometer 437 database, it is identified that age groups is significantly related to the frequency of using the Internet to access music and films online. It is also observed that gender is significantly related to frequency of using the Internet to access films online. The choices facing the Portuguese and Spanish age groups are homogeneous regarding the frequency of Internet use to access music but not films online. These results can be an indication that policies concerning the music and films contents should fit the characteristics of each of these age groups in both countries.

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“…The model proposed by [22], on the other hand, combines the point process models with feature based approaches to predict the popularity. Other models like the growth-adoption model by [20], the spatial-temporal heterogeneous Bass model by [30], and the concept drift model by [19], were all proposed for the purpose of popularity predictions.…”

Section: Introductionmentioning

confidence: 99%

Marked self-exciting point process modelling of information diffusion on Twitter

Chen¹,

Tan²

2018

Ann. Appl. Stat.

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Information diffusion occurs on microblogging platforms like Twitter as retweet cascades. When a tweet is posted, it may be retweeted and henceforth further retweeted, and the retweeting process continues iteratively and indefinitely. A natural measure of the popularity of a tweet is the number of retweets it generates. Accurate predictions of tweet popularity can assist Twitter to rank contents more effectively and facilitate the assessment of potential for marketing and campaigning strategies. In this paper, we propose a model called the Marked Self-Exciting Process with Time-Dependent Excitation Function, or MaSEPTiDE for short, to model the retweeting dynamics and to predict the tweet popularity. Our model does not require expensive feature engineering but is capable of leveraging the observed dynamics to accurately predict the future evolution of retweet cascades. We apply our proposed methodology on a large amount of Twitter data and report substantial improvement in prediction performance over existing approaches in the literature.

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Predicting the popularity growth of online content: Model and algorithm

Cited by 30 publications

References 14 publications

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

Comparing the Consumption of Musical and Films Online between Generations X and Y in Portugal and Spain

Marked self-exciting point process modelling of information diffusion on Twitter

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