45 research outputs found
Design of Randomized Experiments in Networks
Over the last decade, the emergence of pervasive online and digitally enabled environments has created a rich source of detailed data on human behavior. Yet, the promise of big data has recently come under fire for its inability to separate correlation from causation-to derive actionable insights and yield effective policies. Fortunately, the same online platforms on which we interact on a day-to-day basis permit experimentation at large scales, ushering in a new movement toward big experiments. Randomized controlled trials are the heart of the scientific method and when designed correctly provide clean causal inferences that are robust and reproducible. However, the realization that our world is highly connected and that behavioral and economic outcomes at the individual and population level depend upon this connectivity challenges the very principles of experimental design. The proper design and analysis of experiments in networks is, therefore, critically important. In this work, we categorize and review the emerging strategies to design and analyze experiments in networks and discuss their strengths and weaknesses
Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia
Open collaboration platforms have fundamentally changed the way that knowledge is produced, disseminated, and consumed. In these systems, contributions arise organically with little to no central governance. Although such decentralization provides many benefits, a lack of broad oversight and coordination can leave questions of information poverty and skewness to the mercy of the system’s natural dynamics. Unfortunately, we still lack a basic understanding of the dynamics at play in these systems and specifically, how contribution and attention interact and propagate through information networks. We leverage a large-scale natural experiment to study how exogenous content contributions to Wikipedia articles affect the attention that they attract and how that attention spills over to other articles in the network. Results reveal that exogenously added content leads to significant, substantial, and long-term increases in both content consumption and subsequent contributions. Furthermore, we find significant attention spillover to downstream hyperlinked articles. Through both analytical estimation and empirically informed simulation, we evaluate policies to harness this attention contagion to address the problem of information poverty and skewness. We find that harnessing attention contagion can lead to as much as a twofold increase in the total attention flow to clusters of disadvantaged articles. Our findings have important policy implications for open collaboration platforms and information networks
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
Pump It Out! The Effect of Transmitter Activity on Content Propagation in Social Media
People share billions of pieces of content such as news, videos, and photos through social media every day. Marketers are interested in the extent to which such content propagates and, importantly, which factors make widespread propagation more likely. Extant research considers various factors, such as content attributes (e.g., newness), source traits (e.g., expertise), and network structure (e.g., connectivity). This research builds on prior work by introducing a novel behavior-focused transmitter characteristic that is positively associated with content propagation in social media: activity, or how frequently a person transmits content. Evidence for this effect comes from five studies and different paradigms. First, two studies using data from large social media platforms (Twitter and LiveJournal) show that content posted by higher-activity transmitters—whom we refer to as “social pumps”—propagates more than content posted by lower-activity transmitters. Second, three experiments explore the mechanism driving this effect, showing that social media users receiving content from a social pump are more likely to retransmit it (a necessary behavior for achieving aggregate-level propagation) because they infer that content from a social pump is more likely to be current, and therefore more attractive as something to pass along through retransmission
Exploring the Complex Pattern of Information Spreading in Online Blog Communities
Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors
Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions
In online communities, antisocial behavior such as trolling disrupts
constructive discussion. While prior work suggests that trolling behavior is
confined to a vocal and antisocial minority, we demonstrate that ordinary
people can engage in such behavior as well. We propose two primary trigger
mechanisms: the individual's mood, and the surrounding context of a discussion
(e.g., exposure to prior trolling behavior). Through an experiment simulating
an online discussion, we find that both negative mood and seeing troll posts by
others significantly increases the probability of a user trolling, and together
double this probability. To support and extend these results, we study how
these same mechanisms play out in the wild via a data-driven, longitudinal
analysis of a large online news discussion community. This analysis reveals
temporal mood effects, and explores long range patterns of repeated exposure to
trolling. A predictive model of trolling behavior shows that mood and
discussion context together can explain trolling behavior better than an
individual's history of trolling. These results combine to suggest that
ordinary people can, under the right circumstances, behave like trolls.Comment: Best Paper Award at CSCW 201
Origins of power-law degree distribution in the heterogeneity of human activity in social networks
The probability distribution of number of ties of an individual in a social
network follows a scale-free power-law. However, how this distribution arises
has not been conclusively demonstrated in direct analyses of people's actions
in social networks. Here, we perform a causal inference analysis and find an
underlying cause for this phenomenon. Our analysis indicates that heavy-tailed
degree distribution is causally determined by similarly skewed distribution of
human activity. Specifically, the degree of an individual is entirely random -
following a "maximum entropy attachment" model - except for its mean value
which depends deterministically on the volume of the users' activity. This
relation cannot be explained by interactive models, like preferential
attachment, since the observed actions are not likely to be caused by
interactions with other people.Comment: 23 pages, 5 figure