Lilian Weng

Competition among memes in a world with limited attention

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

Virality Prediction and Community Structure in Social Networks

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

The role of information diffusion in the evolution of social networks

Every day millions of users are connected through online social networks, generating a rich trove of data that allows us to study the mechanisms behind human interactions. Triadic closure has been treated as the major mechanism for creating social links: if Alice follows Bob and Bob follows Charlie, Alice will follow Charlie. Here we present an analysis of longitudinal micro-blogging data, revealing a more nuanced view of the strategies employed by users when expanding their social circles. While the network structure affects the spread of information among users, the network is in turn shaped by this communication activity. This suggests a link creation mechanism whereby Alice is more likely to follow Charlie after seeing many messages by Charlie. We characterize users with a set of parameters associated with different link creation strategies, estimated by a Maximum-Likelihood approach. Triadic closure does have a strong effect on link formation, but shortcuts based on traffic are another key factor in interpreting network evolution. However, individual strategies for following other users are highly heterogeneous. Link creation behaviors can be summarized by classifying users in different categories with distinct structural and behavioral characteristics. Users who are popular, active, and influential tend to create traffic-based shortcuts, making the information diffusion process more efficient in the network.

Topicality and Impact in Social Media: Diverse Messages, Focused Messengers

We have a limited understanding of the factors that make people influential and topics popular in social media. Are users who comment on a variety of matters more likely to achieve high influence than those who stay focused? Do general subjects tend to be more popular than specific ones? Questions like these demand a way to detect the topics hidden behind messages associated with an individual or a keyword, and a gauge of similarity among these topics. Here we develop such an approach to identify clusters of similar hashtags in Twitter by detecting communities in the hashtag co-occurrence network. Then the topical diversity of a user’s interests is quantified by the entropy of her hashtags across different topic clusters. A similar measure is applied to hashtags, based on co-occurring tags. We find that high topical diversity of early adopters or co-occurring tags implies high future popularity of hashtags. In contrast, low diversity helps an individual accumulate social influence. In short, diverse messages and focused messengers are more likely to gain impact.

Design of social games for collecting reliable semantic annotations

We present two social tagging games based on the Chain Model for object association. GiveALink Slider and Great Minds Think Alike harness human power to generate large streams of social tagging data. Such social annotations are utilized to help people organize Web resources and infer semantic relationship, which in turn can enhance Web applications such as search, recommendation, navigation, and categorization. The two games leverage several design features as well as external social media resources to create entertaining incentives for the players to tag sites and multimedia objects. Preliminary analysis of data generated by these games suggest that the proposed model can be effective for the collection of annotations that are novel, have high quality, and lead to reliable semantics.

The chain model for social tagging game design

We introduce the Chain Model for object association games, and two social tagging games based on this model. GiveALink Slider and Great Minds Think Alike harness human power to generate large streams of high-quality social tagging data. Such social annotations are utilized to help people organize Web resources and infer semantic relationship, which in turn can enhance Web applications such as search, recommendation, navigation, and categorization. The two games leverage several design features as well as external social media resources to create entertaining incentives for the players to generate reliable annotation data.

Emergent semantics from game-induced folksonomies

We describe the GiveALink Slider, an experimental social tagging game designed with the purpose of generating meaningful and useful annotations to improve upon the drawbacks of existing folksonomies. Knowledge, in the form of reliable annotations, is validated and accumulated through the implicit interactions among multiple players. In this paper we explore the hypothesis that such a game can improve both the quality and quantity of social annotation data. Our evaluation of game-induced annotations shows that games may improve on the semantic structure of existing folksonomies from several perspectives, including searchability, novelty, and coherence. Games can therefore play a valuable role in the collection of helpful annotations, by leveraging human power and specific game design features.

Attention on Weak Ties in Social and Communication Networks

Granovetter’s weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter’s work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter’s hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties—possibly reflecting the postulated informational purposes of such ties—but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform’s typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content.

Scalable Detection of Viral Memes from Diffusion Patterns

Social media and social networking platforms have flourished with the rapid development of mobile technology and the ubiquitous use of the Internet. As a result, memes, or pieces of information spreading from person to person, can be reshared among users quickly and gain huge popularity. As viral memes have tremendous social and economic impact, detecting these viral memes at their early stages of spread is a worthy, yet challenging problem. Here we review the literature on predicting viral memes, and present empirical results from Twitter and Tumblr datasets. We demonstrate how diffusion patterns of memes, in the context of network communities, play an important role in predicting virality. We show that it is feasible to obtain predictive features based on community structure even at the massive scales that common social media services need to process. Our results may not only enable practitioners to make predictions about meme diffusion, but also help researchers understand how and why different factors, in particular diffusion patterns in communities, affect online virality.

Computational Analysis of Collective Behaviors via Agent-Based Modeling

Agent-based modeling (ABM) is a common computational analysis tool to study system dynamics. In the framework of ABM, the system consists of multiple autonomous and interacting agents. We can explore emergent collective patterns by simulating the individual operations and interactions between agents. As a case study, we present an experiment using an agent-based model to study how competition for limited user attention in a social network results in collective patterns of meme popularity. The model is inspired by the long tradition that represents information spreading as an epidemic process, where infection is passed along the edges of the underlying social network. The model also builds upon empirical observations on how individual humans behave online. The combination of social network structure and finite agent attention is sufficient for the emergence of broad diversity in meme popularity and lifetime. The case study illustrates how one can analyze the kind of emergent human computation that makes some memes very popular.