Feld's friendship paradox states that "your friends have more friends than you, on average." This paradox arises because extremely popular people, despite being rare, are overrepresented when averaging over friends. Using a sample of the Twitter firehose, we confirm that the friendship paradox holds for >98 of Twitter users. Because of the directed nature of the follower graph on Twitter, we are further able to confirm more detailed forms of the friendship paradox: everyone you follow or who follows you has more friends and followers than you. This is likely caused by a correlation we demonstrate between Twitter activity, number of friends, and number of followers. In addition, we discover two new paradoxes: the virality paradox that states "your friends receive more viral content than you, on average," and the activity paradox, which states "your friends are more active than you, on average." The latter paradox is important in regulating online communication. It may result in users having difficulty maintaining optimal incoming information rates, because following additional users causes the volume of incoming tweets to increase super-linearly. While users may compensate for increased information flow by increasing their own activity, users become information overloaded when they receive more information than they are able or willing to process. We compare the average size of cascades that are sent and received by overloaded and underloaded users. And we show that overloaded users post and receive larger cascades and they are poor detector of small cascades.
In this paper, we confirm the friendship paradox exists in Twitter, i.e. a user's friends have more friends on average than the user itself, which has also been observed by Garcia- @cite_16 . Complimenting the work by Garcia-, we indirectly explain why early detection is possible on Twitter. Tweets are not pathogens, i.e., a tweet must be actively propagated to become a viral meme, unlike the flu or other live pathogens which propagate without any conscious effort by the host vector. Hence, network structure alone is insufficient to develop a robustly successful application of the friendship paradox to understanding social contagion. We report that the missing connection is the high correlation between activity and connectivity.
@cite_16
1959141096 2102365618 2097797505 2289705509
Feld's friendship paradox states that "your friends have more friends than you, on average." This paradox arises because extremely popular people, despite being rare, are overrepresented when averaging over friends. Using a sample of the Twitter firehose, we confirm that the friendship paradox holds for >98 of Twitter users. Because of the directed nature of the follower graph on Twitter, we are further able to confirm more detailed forms of the friendship paradox: everyone you follow or who follows you has more friends and followers than you. This is likely caused by a correlation we demonstrate between Twitter activity, number of friends, and number of followers. In addition, we discover two new paradoxes: the virality paradox that states "your friends receive more viral content than you, on average," and the activity paradox, which states "your friends are more active than you, on average." The latter paradox is important in regulating online communication. It may result in users having difficulty maintaining optimal incoming information rates, because following additional users causes the volume of incoming tweets to increase super-linearly. While users may compensate for increased information flow by increasing their own activity, users become information overloaded when they receive more information than they are able or willing to process. We compare the average size of cascades that are sent and received by overloaded and underloaded users. And we show that overloaded users post and receive larger cascades and they are poor detector of small cascades. If a false rumor propagates via Twitter, while the truth propagates between friends in Facebook, which one will prevail? This question captures the essence of the problem we address here. We study the intertwined propagation of two competing "memes" (or viruses, rumors, products etc.) in a composite network. A key novelty is the use of a composite network, which in its simplest model is defined as a single set of nodes with two distinct types of edges interconnecting them. Each meme spreads across the composite network in accordance to an SIS-like propagation model (a flu-like infection-recovery). To study the epidemic behavior of our system, we formulate it as a non-linear dynamic system (NLDS). We develop a metric for each meme that is based on the eigenvalue of an appropriately constructed matrix and argue that this metric plays a key role in determining the "winning" meme. First, we prove that our metric determines the tipping point at which both memes become extinct eventually. Second, we conjecture that the meme with the strongest metric will most likely prevail over the other, and we show evidence of that via simulations in both real and synthetic composite networks. Our work is among the first to study the interplay between two competing memes in composite networks. A microblogging service like Twitter continues to surge in importance as a means of sharing information in social networks. In the medical domain, several works have shown the potential of detecting public health events (i.e., infectious disease outbreaks) using Twitter messages or tweets. Given its real-time nature, Twitter can enhance early outbreak warning for public health authorities in order that a rapid response can take place. Most of previous works on detecting outbreaks in Twitter simply analyze tweets matched disease names and or locations of interests. However, the effectiveness of such method is limited for two main reasons. First, disease names are highly ambiguous, i.e., referring slangs or non health-related contexts. Second, the characteristics of infectious diseases are highly dynamic in time and place, namely, strongly time-dependent and vary greatly among different regions. In this paper, we propose to analyze the temporal diversity of tweets during the known periods of real-world outbreaks in order to gain insight into a temporary focus on specific events. More precisely, our objective is to understand whether the temporal diversity of tweets can be used as indicators of outbreak events, and to which extent. We employ an efficient algorithm based on sampling to compute the diversity statistics of tweets at particular time. To this end, we conduct experiments by correlating temporal diversity with the estimated event magnitude of 14 real-world outbreak events manually created as ground truth. Our analysis shows that correlation results are diverse among different outbreaks, which can reflect the characteristics (severity and duration) of outbreaks. Information cascades on social networks, such as retweet cascades on Twitter, have been often viewed as an epidemiological process, with the associated notion of virality to capture popular cascades that spread across the network. The notion of structural virality or average path length has been posited as a measure of global spread. In this paper, we argue that this simple epidemiological view, though analytically compelling, is not the entire story. We first show empirically that the classical SIR diffusion process on the Twitter graph, even with the best possible distribution of infectiousness parameter, cannot explain the nature of observed retweet cascades on Twitter. More specifically, rather than spreading further from the source as the SIR model would predict, many cascades that have several retweets from direct followers, die out quickly beyond that. We show that our empirical observations can be reconciled if we take interests of users and tweets into account. In particular, we consider a model where users have multi-dimensional interests, and connect to other users based on similarity in interests. Tweets are correspondingly labeled with interests, and propagate only in the subgraph of interested users via the SIR process. In this model, interests can be either narrow or broad, with the narrowest interest corresponding to a star graph on the interested users, with the root being the source of the tweet, and the broadest interest spanning the whole graph. We show that if tweets are generated using such a mix of interests, coupled with a varying infectiousness parameter, then we can qualitatively explain our observation that cascades die out much more quickly than is predicted by the SIR model. In the same breath, this model also explains how cascades can have large size, but low "structural virality" or average path length.