Predicting the popularity of online content

Szabó, Gábor; Huberman, Bernardo A.

doi:10.1145/1787234.1787254

Cited by 850 publications

(699 citation statements)

References 23 publications

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“…Of the many factors contributing to the exponential and significant increase in Internet usage, most researchers agree that the growth of Web content has made the most critical contribution (Huang, 2008;Leighton, 2009;Szabo & Huberman, 2010). Unlike offline mediums of communication (e.g., print media [newspapers, magazines]), the Web (online medium of communication) possess characteristics of high connectivity, interactivity, and global reach (Mulhern, 2009;Scarpi, 2012;Singh, Bansal, & Kaur, 2012).…”

Section: Introductionmentioning

confidence: 99%

The Effects of Web Atmospherics on Entertainment Gratification and Web Irritation: Some Empirical Evidence from Online Shopping

Lim¹

2013

MAS

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The difficulty in converting web browsers into actual online customers remains a problem for many online retailers. While previous studies have suggested that online retailers should strive to increase entertainment gratification and reduce web irritation among online consumers, they lack empirical evidence to support the manipulation of web atmospherics toward this end. The research presented here establishes and tests the conceptual links of web atmospherics, entertainment gratification and web irritation. Data were collected using survey and mall-intercept systematic sampling approaches and analyzed using structural equation modeling. The results indicate that well-designed dynamic web atmospherics had a significant effect in increasing entertainment gratification and reducing web irritation. Implications and recommendations from the research findings are also presented.

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Section: Introductionmentioning

confidence: 99%

The Effects of Web Atmospherics on Entertainment Gratification and Web Irritation: Some Empirical Evidence from Online Shopping

Lim¹

2013

MAS

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“…Thus, the linear growth in popularity is induced by the competition between memes, in contrast to the exponential growth at earlier times [ Fig. 2(a)] when the memes were not competing for the same resources [7,29].…”

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confidence: 99%

Competition-Induced Criticality in a Model of Meme Popularity

et al. 2014

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Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism that poises the system at criticality. The popularity growth of each meme is described by a critical branching process, and asymptotic analysis predicts power-law distributions of popularity with very heavy tails (exponent α < 2, unlike preferential-attachment models), similar to those seen in empirical data. DOI: 10.1103/PhysRevLett.112.048701 PACS numbers: 89.65.-s, 05.65.+b, 89.75.Fb, 89.75.Hc When people select from multiple items of roughly equal value, some items quickly become extremely popular, while other items are chosen by relatively few people [1]. The probability P n ðtÞ that a random item has been selected n times by time t is often observed to have a heavytailed distribution (n is called the popularity of the item at time t). In examples where the items are baby names [2], apps on Facebook [3], retweeted URLs or hashtags on Twitter [4-6], or video views on YouTube [7], the popularity distribution is found to scale approximately as a power law P n ∼ n −α over several decades. The exponent α in all these examples is less than two, and typically has a value close to 1.5. This range of α values is notably distinct from those obtainable from cumulative-advantage or preferential-attachment models of the Yule-Simon type-as used to describe power-law degree distributions of networks, for example [8-11]-which give α ≥ 2. Interestingly, the value α ¼ 1.5 is also found for the power-law distribution of avalanche sizes in self-organized criticality (SOC) models [12,13], suggesting the possibility that the heavy-tailed distributions of popularity in the examples above are due to the systems being somehow poised at criticality.In this Letter, we present an analytically tractable model of selection behavior, based on simplifying the model of Weng et al. [14] for the spreading of memes on a social network. We show that, in certain limits, the system is automatically poised at criticality-in the sense that meme popularities are described by a critical branching process [15]-and that the criticality can be ascribed to the competition between memes for the limited resource of user attention. We dub this mechanism "competitioninduced criticality" (CIC) and investigate the impact of the social network topology (degree distribution) and the age of the memes upon the distribution of meme popularities. We show that CIC gives rise to heavy-tailed distributions very similar to the distributions of avalanche sizes in SOC models [16,17], even though our competition mechanism is quite different from the sandpile paradigm of SOC. This Letter may, therefore, be of interest in other areas where SOC-like critical phenomena have been observed in experiments or simulations, such as economic models of competing firms [18,19], the evolution and extinction of competing species [20][21][22], and neural activity in ...

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“…Cha et al [20,23] found that there is a strong correlation between the popularity of a video after two days and after ninety days. These observations were supported by a study performed by Szabo et al [5]. More recently, Chatzopoulou et al [24] studied the correlation between popularity and a wider range of metrics.…”

Section: Related Workmentioning

confidence: 52%

“…Several efforts have been made to apply this theory to the prediction of multimedia content popularity [4,5]. However, to our knowledge, these predictions have never been integrated into an actual cache replacement strategy.…”

Section: Introductionmentioning

confidence: 99%

Towards a predictive cache replacement strategy for multimedia content

Famaey

Iterbeke

Wauters

et al. 2013

Journal of Network and Computer Applications

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In recent years, telecom operators have been moving away from traditional broadcast-driven television, towards IP-based interactive and on-demand multimedia services. Consequently, multicast is no longer sufficient to limit the amount of generated traffic in the network. In order to prevent an explosive growth in traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Traditional caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this article, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The proposed generic popularity prediction algorithm is capable of predicting future content popularity, independent of specific content and service characteristics. Additionally, a novel cache replacement strategy, which employs the popularity prediction algorithm when making its decisions, is introduced. A detailed evaluation, based on simulation results using trace files from an actual deployed Video on Demand service, was performed. The evaluation results are used to determine the merits of popularity-based caching compared to traditional strategies. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated. Results show that the proposed prediction-based caching strategy has the potential to significantly outperform state-of-the-art traditional strategies. Specifically, the evaluated Video on Demand scenario showed a performance increase of up to 20% in terms of cache hit rate.

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Predicting the popularity of online content

Cited by 850 publications

References 23 publications

The Effects of Web Atmospherics on Entertainment Gratification and Web Irritation: Some Empirical Evidence from Online Shopping

The Effects of Web Atmospherics on Entertainment Gratification and Web Irritation: Some Empirical Evidence from Online Shopping

Competition-Induced Criticality in a Model of Meme Popularity

Towards a predictive cache replacement strategy for multimedia content

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