91,183 research outputs found
On the Role of Social Identity and Cohesion in Characterizing Online Social Communities
Two prevailing theories for explaining social group or community structure
are cohesion and identity. The social cohesion approach posits that social
groups arise out of an aggregation of individuals that have mutual
interpersonal attraction as they share common characteristics. These
characteristics can range from common interests to kinship ties and from social
values to ethnic backgrounds. In contrast, the social identity approach posits
that an individual is likely to join a group based on an intrinsic
self-evaluation at a cognitive or perceptual level. In other words group
members typically share an awareness of a common category membership.
In this work we seek to understand the role of these two contrasting theories
in explaining the behavior and stability of social communities in Twitter. A
specific focal point of our work is to understand the role of these theories in
disparate contexts ranging from disaster response to socio-political activism.
We extract social identity and social cohesion features-of-interest for large
scale datasets of five real-world events and examine the effectiveness of such
features in capturing behavioral characteristics and the stability of groups.
We also propose a novel measure of social group sustainability based on the
divergence in group discussion. Our main findings are: 1) Sharing of social
identities (especially physical location) among group members has a positive
impact on group sustainability, 2) Structural cohesion (represented by high
group density and low average shortest path length) is a strong indicator of
group sustainability, and 3) Event characteristics play a role in shaping group
sustainability, as social groups in transient events behave differently from
groups in events that last longer
Who let the trolls out? Towards understanding state-sponsored trolls
Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused ?trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's in- formation ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence detection is not straightforward. Using Hawkes Processes, we quantify the influence these accounts have on pushing URLs on four platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab. In general, Russian trolls were more influential and efficient in pushing URLs to all the other platforms with the exception of /pol/ where Iranians were more influential. Finally, we release our source code to ensure the reproducibility of our results and to encourage other researchers to work on understanding other emerging kinds of state-sponsored troll accounts on Twitter.https://arxiv.org/pdf/1811.03130.pdfAccepted manuscrip
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
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