SpringerOpen Newsletter

Receive periodic news and updates relating to SpringerOpen.

Open Access Open Badges Regular article

Long trend dynamics in social media

Chunyan Wang1 and Bernardo A Huberman2

Author Affiliations

1 Department of Applied Physics, Stanford University, Stanford, CA, USA

2 Social Computing Lab, HP Labs, Palo Alto, California, USA

EPJ Data Science 2012, 1:2  doi:10.1140/epjds2

The electronic version of this article is the complete one and can be found online at: http://www.epjdatascience.com/content/1/1/2

Received:23 January 2012
Accepted:18 May 2012
Published:18 May 2012

© 2012 Wang and Huberman; licensee Springer

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A main characteristic of social media is that its diverse content, copiously generated by both standard outlets and general users, constantly competes for the scarce attention of large audiences. Out of this flood of information some topics manage to get enough attention to become the most popular ones and thus to be prominently displayed as trends. Equally important, some of these trends persist long enough so as to shape part of the social agenda. How this happens is the focus of this paper. By introducing a stochastic dynamical model that takes into account the user’s repeated involvement with given topics, we can predict the distribution of trend durations as well as the thresholds in popularity that lead to their emergence within social media. Detailed measurements of datasets from Twitter confirm the validity of the model and its predictions.

1 Introduction

The past decade has witnessed an explosive growth of social media, creating a competitive environment where topics compete for the attention of users [1,2]. A main characteristic of social media is that both users and standard media outlets generate content at the same time in the form of news, videos and stories, leading to a flood of information from which it is hard for users to sort out the relevant pieces to concentrate on [3,4]. User attention is critical for the understand of how problems in culture, decision making and opinion formation evolve [5-7]. Several studies have shown that attention allocated to on-line content is distributed in a highly skewed fashion [8-11]. While most documents receive a negligible amount of attention, a few items become extremely popular and persist as public trends for long a period of time [12-14]. Recent studies have focused on the dynamical growth of attention on different kinds of social media, including Digg [15-17], Youtube [18], Wikipedia [19-21] and Twitter [14,22-24]. The time-scale over which content persists as a topic in these media also varies on a scale from hours to years. In the case of news and stories, content spreads on the social network until its novelty decays [15]. In information networks like Wikipedia, where a document remains alive for months and even years, popularity is governed by bursts of sudden events and is explained by the rank shift model [19].

While previous work has successfully addressed the growth and decay of news and topics in general, a remaining problem is why some of the topics stay popular for longer periods of time than others and thus contribute to the social agenda. In this paper, we focus on the dynamics of long trends and their persistence within social media. We first introduce a dynamic model of attention growth and derive the distribution of trend durations for all topics. By analyzing the resonating nature of the content within the community, we provide a threshold criterion that successfully predicts the long term persistence of social trends. The predictions of the model are then compared with measurements taken from Twitter, which as we show provides a validation of the proposed dynamics.

This paper is structured as follows. In Section 2 we describe our model for attention growth and the persistence of trends. Section 3 describes the data-set and the collection strategies used in the study, whereas Section 4 discusses the measurements made on data-sets from Twitter and compares them with the predictions of the model. Section 5 concludes with a summary of our findings and future directions.

2 Model

On-line micro-blogging and social service websites enable users to read and send text-based messages to certain topics of interest. The popularity of these topics is commonly measured by the number of postings about these topics [15,19]. For instance on Twitter, Digg and Youtube, users post their thoughts on topics of interest in the form of tweets and comments. One special characteristic of social media that has been ignored so far is that users can contribute to the popularity of a topic more than once. We take this into account by denoting first posts on a certain topic from a certain user by the variable First Time Post (FTP). If the same user posts on the topic more than once, we call it a Repeated Post (RP). In what follows, we first look at the growth dynamics of FTP.

When a topic first catches people’s attention, a few people may further pass it on to others in the community. If we denote the cumulative number of FTP mentioning the topic at time t by <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M1','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M1">View MathML</a>, the growth of attention can be described by <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M2','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M2">View MathML</a>, where the <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M3','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M3">View MathML</a> are assumed to be small, positive, independent and identically distributed random variables with mean μ and variance <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M4','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M4">View MathML</a>. For small <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M5','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M5">View MathML</a>, the equation can be approximated as:

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M6','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M6">View MathML</a>


Taking logarithms on both sides, we obtain <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M7','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M7">View MathML</a>. Applying the central limit theorem to the sum, it follows that the cumulative count of FTP should obey a log-normal distribution.

We now consider the persistence of social trends. We use the variable vitality, <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M8','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M8">View MathML</a>, as a measurement of popularity, and assume that if the vitality of a topic falls below a certain threshold <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M9','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M9">View MathML</a>, the topic stops trending. Thus

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M10','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M10">View MathML</a>


The probability of ceasing to trend at the time interval s is equal to the probability that <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M11','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M11">View MathML</a> is lower than a threshold value <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M12','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M12">View MathML</a>, which can be written as:

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M13','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M13">View MathML</a>


where <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M14','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M14">View MathML</a> is the cumulative distribution function of the random variable χ. We are thus able to determine the threshold value from <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M15','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M15">View MathML</a> if we know the distribution of the random variable χ. Notice that if χ is independent and identically distributed, it follows that the distribution of trending durations is given by a geometric distribution with <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M16','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M16">View MathML</a>. The expected trending duration of a topic, <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M17','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M17">View MathML</a>, is therefore given by

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M18','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M18">View MathML</a>


Thus far we have only considered the impact of FTP on social trends by treating all topics as identical to each other. To account for the resonance between users and specific topics we now include the RP into the dynamics. We define the instantaneous number of FTP posted in the time interval t as <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M19','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M19">View MathML</a>, and the repeated posts, RP, in the time interval t as <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M20','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M20">View MathML</a>. Similarly we denote the cumulative number of all posts-including both FTP and RP-as <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M21','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M21">View MathML</a>. The resonance level of fans with a given topic is measured by <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M22','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M22">View MathML</a>, and we define the expected value of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M23','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M23">View MathML</a>, <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M24','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M24">View MathML</a> as the active-ratio <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M25','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M25">View MathML</a>.

We can simplify the dynamics by assuming that <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M23','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M23">View MathML</a> is independent and uniformly distributed on the interval <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M27','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M27">View MathML</a>. It then follows that the increment of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M21','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M21">View MathML</a> is given by the sum of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M19','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M19">View MathML</a> and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M20','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M20">View MathML</a>. We thus have

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M31','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M31">View MathML</a>


And also

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M32','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M32">View MathML</a>


We approximate <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M33','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M33">View MathML</a> by <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M34','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M34">View MathML</a>. Going back to Equation 5, we have

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M35','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M35">View MathML</a>


From this, it follows that the dynamics of the full attention process is determined by the two independent random variables, μ and χ. Similarly to the derivation of Equation 3, the topic is assumed to stop trending if the value of either one of the random variables governing the process falls below the thresholds <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M9','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M9">View MathML</a> and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M37','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M37">View MathML</a>, respectively. One point worth mentioning here is that, <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M9','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M9">View MathML</a> and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M37','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M37">View MathML</a> are system parameters, i.e. not dependent on the topic, but only on the studied medium. The probability of ceasing to trend, defined as <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M40','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M40">View MathML</a>, is now given by

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M41','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M41">View MathML</a>


<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M42','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M42">View MathML</a>. The expected value of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M43','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M43">View MathML</a> for any topic q is given by

<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M44','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M44">View MathML</a>


Which states that the persistent duration of trends associated with given topics is expected to scale linearly with the topic users’ active-ratio. From this result it follows that one can predict the trend duration for any topic by measuring its user active-ratio after the values of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M9','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M9">View MathML</a> and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M37','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M37">View MathML</a> are determined from empirical observations.

3 Data

To test the predictions of our dynamic model, we analyzed data from Twitter, an extremely popular social network website used by over 200 million users around the world. Its interface of allows users to post short messages, known as tweets, that can be read and retweeted by other Twitter users. Users declare the people they follow, and they get notified when there is a new post from any of these people. A user can also forward the original post of another user to his followers by the re-tweet mechanism.

In our study, the cumulative count of tweets and re-tweets that are related to a certain topic was used as a proxy for the popularity of the topic. On the front page of Twitter there is also a column named trends that presents the few keywords or sentences that are most frequently mentioned in Twitter at a given moment. The list of popular topics in the trends column is updated every few minutes as new topics become popular. We collected the topics in the trends column by performing an API query every 20 minutes. For each of the topics in the trending column, we used the Search API function to collect the full list of tweets and re-tweets related to the topic over the past 20 minutes. We also collected information about the author of the post, identified by a unique user-id, the text of the post and the time of its posting. We thus obtained a dataset of 16.32 million posts on 3361 different topics. The longest trending topic we observed had a length of 14.7 days. We found that of all the posts in our dataset, 17% belonged to the RP category.

4 Results

We start by analyzing the distribution of N from our data-set. We found out that <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M47','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M47">View MathML</a> follows a log-normal distribution, as can be seen from Figure 1. The Q-Q plot in Figure 1 follows a straight line. Different values of t yield similar results. The Kolmogorov-Smirnov normality test of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M48','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M48">View MathML</a> with mean 3.5577 and standard deviation 0.3266 yields a p-value of 0.0838. At a significance level of 0.05, the test fails to reject the null hypothesis that <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M49','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M49">View MathML</a> follows normal distribution, a result which is consistent with Equation 1.

thumbnailFigure 1. Q-Q plot of<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M49','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M49">View MathML</a>. The straight line shows that the data follows a lognormal distribution with a slightly shorter tail.

We also measured the distribution of χ from <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M51','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M51">View MathML</a>. We found that <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M52','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M52">View MathML</a> follows a normal distribution with mean equal to −1.4522 and a standard deviation value of 0.6715, as shown in Figure 2. The Kolmogorov-Smirnov normality test statistic gives a high p-value of 0.5346. The mean value of χ is 0.0353, which is small for the approximations in Equation 1 and Equation 7 to be valid. We also examined the record breaking values of vitality, <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M53','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M53">View MathML</a>, which signal the behavior of the longest lasting trends. From the theory of records, if the values <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M54','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M54">View MathML</a> come from an independent and identical distribution, the number of records that have occurred up to time t, defined as <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M55','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M55">View MathML</a>, should scale linearly with <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M56','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M56">View MathML</a>[26,27]. As is customary, we say that a new record has been established if the vitality of the trend at the moment is longer than all of the previous observations. As shown in Figure 3, there is a linear scaling relationship between <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M56','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M56">View MathML</a> and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M55','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M55">View MathML</a> for a sample topic “Kim Chul Hee”. The topic kept trending for 14 days on Twitter in September 2010. Similar observations are repeated for other different topics on Twitter. One implication of this observation is that confirms the validity of our assumption that the values of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M59','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M59">View MathML</a> are independent and identically distributed.

thumbnailFigure 2. Density plot and Q-Q plot of<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M52','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M52">View MathML</a>.(a) Density plot of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M52','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M52">View MathML</a> over different t and social trends. (b) Q-Q plot of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M52','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M52">View MathML</a>.

thumbnailFigure 3. The linear scaling relationship between<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M55','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M55">View MathML</a>and<a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M64','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M64">View MathML</a>of topic ‘Kim Chul Hee’, a Korean pop star. The number of records that have occurred up to time t scales linearly with <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M56','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M56">View MathML</a>.

Next we turn our attention to the distribution of durations of long trends. As shown in Figure 4 and Figure 5, a linear fit of trend duration as a function of density in a logarithmic scale suggests an exponential family, which is consistent with Equation 4. The red line in Figure 4 gives a linear fitting with R-square 0.9112. From the log-log scale plot in Figure 5, we observe that the distribution deviates from a power law, which is a characteristic of social trends that originate from news on social media [25]. From the distribution of trending times, p is estimated to have a value of 0.12. Together with the measured distribution of χ and Equation 3, we can estimate the value of θ to be 1.0132.

thumbnailFigure 4. Semi-log plot of trending duration density. The straight line suggests an exponential family of the trending time distribution.

thumbnailFigure 5. Density plot of trending duration in log-log scale. The distribution of duration deviates from a power law.

We can also determine the expected duration of trend times stemming from the impact of active-ratio. The frequency count of active-ratios over different topics is shown in Figure 6, with a peak at <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M66','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M66">View MathML</a>. This observation suggests that while the ratio is centered around 1.2 for the majority of topics, there are a few topics obtain large amount of repeated attention. This observation may shadow light on existing observations about the highly skewed distribution in attention dynamic studies. As can be seen in Figure 7, the trend duration of different topics scales linearly with the active-ratio, which is consistent with the prediction of Equation 9. The R-square of the linear fitting has a value of 0.98664. From the slope of the linear fit and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M67','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M67">View MathML</a>, and Equation 9 we obtain a value for <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M68','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M68">View MathML</a>. With the value of <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M9','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M9">View MathML</a> and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M37','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M37">View MathML</a>, we are able to predict the expected trend duration of any given topic based on measurements of its active-ratio.

thumbnailFigure 6. Frequency count of active-ratio over all topics. The maximum ratio is 1.2 among all topics.

thumbnailFigure 7. Linear relationship between trending duration and active-ratio in good agreement with the predictions of model.

5 Discussion and conclusion

In this paper we investigated the persistence dynamics of trends in social media. By introducing a stochastic dynamic model that takes into account the user’s repeated involvement with given topics, we are able to predict the distribution of trend durations as well as the thresholds in popularity that lead to the emergence of given topics as trends within social media. The predictions of our mode were confirmed by a careful analysis of a data from Twitter. Furthermore, a linear relationship between the resonance level of users with given topics, and the trending duration of a topic was derived. The proposed model provides a deeper understanding the popularity of on-line contents. Parameters <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M9','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M9">View MathML</a> and <a onClick="popup('http://www.epjdatascience.com/content/1/1/2/mathml/M37','MathML',630,470);return false;" target="_blank" href="http://www.epjdatascience.com/content/1/1/2/mathml/M37">View MathML</a> in our model are system specific and could be calculated from hidden algorithms when applying our model to other on-line social media websites. Possible refinements may include the effect of competition between topics, sudden burst of events, the effect of marketing campaigns and the actively censoring of specific topics [28]. In closing, we note that although the focus in this paper has been on trend dynamics that are featured on social media websites, the framework and model may be suitable to other types of content and off-line trends. The issue raised - that is, trending phenomenon under the impact of user’s repeated involvement - is therefore a general one and should provide ample opportunities for future work.

Competing interests

The authors declare that they have no competing interests.

Author contributions

B.H. and C.W. designed the study and performed research. C.W. and B.H. wrote the paper. All authors read and approved the final manuscript.


We acknowledge useful discussions with S. Asur and G. Szabo. C.W. would like to thank HP Labs for financial support.


  1. McCombs ME, Shaw DL (1993) The evolution of agenda setting research: twenty five years in the marketplace of ideas. Journal of Communication 43(2):68-84 Publisher Full Text OpenURL

  2. Falkinger J (2008) Limited attention as a scarce resource in information-rich economies. Econ J (Lond) 118(532):1596-1620 OpenURL

  3. Agichtein E, Castillo C, Donato D, Gionis A, Mishne G (2008) Finding high-quality content in social media. Proceedings of the international conference on Web search and web data mining (WSDM). OpenURL

  4. Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz 53(1):59-68 Publisher Full Text OpenURL

  5. Zhu J-H (1992) Issue competition and attention distraction: a zero-sum theory of agenda setting. Journal Q 69:825-836 Publisher Full Text OpenURL

  6. Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316(5827):1036-1039 PubMed Abstract | Publisher Full Text OpenURL

  7. Guimerà R, Uzzi B, Spiro J, Amaral LAN (2005) Team assembly mechanisms determine collaboration network structure and team performance. Science 308(5722):697-702 PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  8. Huberman BA, Pirolli PLT, Pitkow JE, Lukose RM (1998) Strong regularities in world wide web surfing. Science 280(5360):95-97 PubMed Abstract | Publisher Full Text OpenURL

  9. Johansen A, Sornette D (2000) Download relaxation dynamics on the WWW following newspaper publication of URL. Physica A 276(1-2):338-345 Publisher Full Text OpenURL

  10. Huberman BA (2001) The laws of the web: patterns in the ecology of information. MIT Press, Massachusetts. OpenURL

  11. Vázquez A, et al. (2006) Modeling bursts and heavy tails in human dynamics. Phys Rev E 73: OpenURL

  12. Neuman WR (1990) The threshold of public attention. Public Opin Q 54:159-176 Publisher Full Text OpenURL

  13. Klamer A, Van Dalen HP (2002) Attention and the art of scientific publishing. J Econ Methodol 9(3):289-315 Publisher Full Text OpenURL

  14. Becker H, Naaman M, Gravano L (2011) Beyond trending topics: real-world event identification on Twitter. Proceedings of 15th international conference on Weblogs and Social Media (ICWSM). OpenURL

  15. Wu F, Huberman BA (2007) Novelty and collective attention. Proc Natl Acad Sci USA 105:17599 OpenURL

  16. Leskovec J, Backstrom L, Kleinberg J (2009) Meme-tracking and the dynamics of the news cycle. International conference on knowledge discovery and data mining (KDD)

  17. Lerman K, Hogg T (2010) Using a model of social dynamics to predict popularity of news. Proceedings of 19th international World Wide Web conference (WWW). OpenURL

  18. Crane R, Sornette D (2008) Robust dynamic classes revealed by measuring the response function of a social system. Proc Natl Acad Sci USA 105:15649 PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  19. Ratkiewicz J, Fortunato S, Flammini A, Menczer F, Vespignani A (2010) Characterizing and modeling the dynamics of online popularity. Phys Rev Lett 105: OpenURL

  20. Capocci A, Servedio VDP, Colaiori F, Buriol LS, Donato D, Leonardi S, Caldarelli G (2006) Preferential attachment in the growth of social networks: the Internet encyclopedia Wikipedia. Phys Rev E 74: OpenURL

  21. Zlatic V, Bozicevic M, Stefancic H, Domazetl M (2006) Wikipedias: collaborative web-based encyclopedias as complex networks. Phys Rev E 74: OpenURL

  22. Jansen BJ, Zhang M, Sobel K, Chowdhury A (2009) Twitter power: tweets as electronic word of mouth. J Am Soc Inf Sci 60(11):2169-2188 Publisher Full Text OpenURL

  23. Lee K, Palsetia D, Narayanan R, Patwary MMA, Agrawal A, Choudhary A (2011) Twitter trending topic classification. 11th IEEE international conference on data mining workshops (ICDMW)

  24. Gonçalves B, Perra N, Vespignani A (2011) Modeling users’ activity on Twitter networks: validation of Dunbar’s number. PLoS ONE 6(8): OpenURL

  25. Sitaram A, Huberman BA, Szabo G, Wang C (2011) Trends in Social Media: persistence and decay. Proceedings of 15th international conference on Weblogs and Social Media (ICWSM). OpenURL

  26. Redner S, Petersen MR (2006) Role of global warming on the statistics of record-breaking temperatures. Phys Rev E 74: OpenURL

  27. Krug J (2007) Records in a changing world. J Stat Mech.



  28. Sydell L (2011) How Twitter’s trending algorithm picks its topics. http://www.npr.org/2011/12/07/143013503/how-twitters-trending-algorithm-picks-its-topics