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Characterizing Online User Dynamics Through the Lens of Misinformation Exposure and Community Loyalty
The proliferation of online social networks brings more and more users together and provides abundant content for them to explore as individuals or groups. As a crucial part of online social networks, user dynamics brings energy and diversity to the platforms. Meanwhile, different societal issues emerge from it. Plenty of behavior provides us with a good amount of resources to understand human and society.
In this dissertation, we have conducted work studying online user dynamics in two aspects. In the first part, we investigate the potential impact of misinformation on individual users through their behavioral change after exposure to online misinformation. Behavioral metrics are developed to measure the change. In the second part, we study user loyalty change as a group member, by characterizing the bandwagon behavior in online sports fan communities. We have developed features and models to effectively predict bandwagon behavior. Our results on the first part provide a deeper dive into misinformation’s impact on users and good insights on helping combating misinformation. The results on the second part provides a comprehensive view of sports fan bandwagon phenomenon, as well as good design implications on improving online community management.</p
Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities
The proliferation of online communities has created exciting opportunities to
study the mechanisms that explain group success. While a growing body of
research investigates community success through a single measure -- typically,
the number of members -- we argue that there are multiple ways of measuring
success. Here, we present a systematic study to understand the relations
between these success definitions and test how well they can be predicted based
on community properties and behaviors from the earliest period of a community's
lifetime. We identify four success measures that are desirable for most
communities: (i) growth in the number of members; (ii) retention of members;
(iii) long term survival of the community; and (iv) volume of activities within
the community. Surprisingly, we find that our measures do not exhibit very high
correlations, suggesting that they capture different types of success.
Additionally, we find that different success measures are predicted by
different attributes of online communities, suggesting that success can be
achieved through different behaviors. Our work sheds light on the basic
understanding of what success represents in online communities and what
predicts it. Our results suggest that success is multi-faceted and cannot be
measured nor predicted by a single measurement. This insight has practical
implications for the creation of new online communities and the design of
platforms that facilitate such communities.Comment: To appear at The Web Conference 201
Characterizing and Modeling the Dynamics of Activity and Popularity
Social media, regarded as two-layer networks consisting of users and items,
turn out to be the most important channels for access to massive information in
the era of Web 2.0. The dynamics of human activity and item popularity is a
crucial issue in social media networks. In this paper, by analyzing the growth
of user activity and item popularity in four empirical social media networks,
i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links
between users and items are more likely to be created by active users and to be
acquired by popular items, where user activity and item popularity are measured
by the number of cross links associated with users and items. This indicates
that users generally trace popular items, overall. However, it is found that
the inactive users more severely trace popular items than the active users.
Inspired by empirical analysis, we propose an evolving model for such networks,
in which the evolution is driven only by two-step random walk. Numerical
experiments verified that the model can qualitatively reproduce the
distributions of user activity and item popularity observed in empirical
networks. These results might shed light on the understandings of micro
dynamics of activity and popularity in social media networks.Comment: 13 pages, 6 figures, 2 table
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
Measuring, Characterizing, and Detecting Facebook Like Farms
Social networks offer convenient ways to seamlessly reach out to large
audiences. In particular, Facebook pages are increasingly used by businesses,
brands, and organizations to connect with multitudes of users worldwide. As the
number of likes of a page has become a de-facto measure of its popularity and
profitability, an underground market of services artificially inflating page
likes, aka like farms, has emerged alongside Facebook's official targeted
advertising platform. Nonetheless, there is little work that systematically
analyzes Facebook pages' promotion methods. Aiming to fill this gap, we present
a honeypot-based comparative measurement study of page likes garnered via
Facebook advertising and from popular like farms. First, we analyze likes based
on demographic, temporal, and social characteristics, and find that some farms
seem to be operated by bots and do not really try to hide the nature of their
operations, while others follow a stealthier approach, mimicking regular users'
behavior. Next, we look at fraud detection algorithms currently deployed by
Facebook and show that they do not work well to detect stealthy farms which
spread likes over longer timespans and like popular pages to mimic regular
users. To overcome their limitations, we investigate the feasibility of
timeline-based detection of like farm accounts, focusing on characterizing
content generated by Facebook accounts on their timelines as an indicator of
genuine versus fake social activity. We analyze a range of features, grouped
into two main categories: lexical and non-lexical. We find that like farm
accounts tend to re-share content, use fewer words and poorer vocabulary, and
more often generate duplicate comments and likes compared to normal users.
Using relevant lexical and non-lexical features, we build a classifier to
detect like farms accounts that achieves precision higher than 99% and 93%
recall.Comment: To appear in ACM Transactions on Privacy and Security (TOPS
Loyalty in Online Communities
Loyalty is an essential component of multi-community engagement. When users
have the choice to engage with a variety of different communities, they often
become loyal to just one, focusing on that community at the expense of others.
However, it is unclear how loyalty is manifested in user behavior, or whether
loyalty is encouraged by certain community characteristics.
In this paper we operationalize loyalty as a user-community relation: users
loyal to a community consistently prefer it over all others; loyal communities
retain their loyal users over time. By exploring this relation using a large
dataset of discussion communities from Reddit, we reveal that loyalty is
manifested in remarkably consistent behaviors across a wide spectrum of
communities. Loyal users employ language that signals collective identity and
engage with more esoteric, less popular content, indicating they may play a
curational role in surfacing new material. Loyal communities have denser
user-user interaction networks and lower rates of triadic closure, suggesting
that community-level loyalty is associated with more cohesive interactions and
less fragmentation into subgroups. We exploit these general patterns to predict
future rates of loyalty. Our results show that a user's propensity to become
loyal is apparent from their first interactions with a community, suggesting
that some users are intrinsically loyal from the very beginning.Comment: Extended version of a paper appearing in the Proceedings of ICWSM
2017 (with the same title); please cite the official ICWSM versio
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