241 research outputs found
Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games
In this work we explore cyberbullying and other toxic behavior in team
competition online games. Using a dataset of over 10 million player reports on
1.46 million toxic players along with corresponding crowdsourced decisions, we
test several hypotheses drawn from theories explaining toxic behavior. Besides
providing large-scale, empirical based understanding of toxic behavior, our
work can be used as a basis for building systems to detect, prevent, and
counter-act toxic behavior.Comment: CHI'1
Mathematical Models and Biological Meaning: Taking Trees Seriously
We compare three basic kinds of discrete mathematical models used to portray
phylogenetic relationships among species and higher taxa: phylogenetic trees,
Hennig trees and Nelson cladograms. All three models are trees, as that term is
commonly used in mathematics; the difference between them lies in the
biological interpretation of their vertices and edges. Phylogenetic trees and
Hennig trees carry exactly the same information, and translation between these
two kinds of trees can be accomplished by a simple algorithm. On the other
hand, evolutionary concepts such as monophyly are represented as different
mathematical substructures are represented differently in the two models. For
each phylogenetic or Hennig tree, there is a Nelson cladogram carrying the same
information, but the requirement that all taxa be represented by leaves
necessarily makes the representation less efficient. Moreover, we claim that it
is necessary to give some interpretation to the edges and internal vertices of
a Nelson cladogram in order to make it useful as a biological model. One
possibility is to interpret internal vertices as sets of characters and the
edges as statements of inclusion; however, this interpretation carries little
more than incomplete phenetic information. We assert that from the standpoint
of phylogenetics, one is forced to regard each internal vertex of a Nelson
cladogram as an actual (albeit unsampled) species simply to justify the use of
synapomorphies rather than symplesiomorphies.Comment: 15 pages including 6 figures [5 pdf, 1 jpg]. Converted from original
MS Word manuscript to PDFLaTe
Enabling Social Applications via Decentralized Social Data Management
An unprecedented information wealth produced by online social networks,
further augmented by location/collocation data, is currently fragmented across
different proprietary services. Combined, it can accurately represent the
social world and enable novel socially-aware applications. We present
Prometheus, a socially-aware peer-to-peer service that collects social
information from multiple sources into a multigraph managed in a decentralized
fashion on user-contributed nodes, and exposes it through an interface
implementing non-trivial social inferences while complying with user-defined
access policies. Simulations and experiments on PlanetLab with emulated
application workloads show the system exhibits good end-to-end response time,
low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of
Foundations of Social Computing, ACM Transactions on Internet Technolog
A Quantitative Approach to Understanding Online Antisemitism
A new wave of growing antisemitism, driven by fringe Web communities, is an
increasingly worrying presence in the socio-political realm. The ubiquitous and
global nature of the Web has provided tools used by these groups to spread
their ideology to the rest of the Internet. Although the study of antisemitism
and hate is not new, the scale and rate of change of online data has impacted
the efficacy of traditional approaches to measure and understand these
troubling trends. In this paper, we present a large-scale, quantitative study
of online antisemitism. We collect hundreds of million posts and images from
alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and
Gab. Using scientifically grounded methods, we quantify the escalation and
spread of antisemitic memes and rhetoric across the Web. We find the frequency
of antisemitic content greatly increases (in some cases more than doubling)
after major political events such as the 2016 US Presidential Election and the
"Unite the Right" rally in Charlottesville. We extract semantic embeddings from
our corpus of posts and demonstrate how automated techniques can discover and
categorize the use of antisemitic terminology. We additionally examine the
prevalence and spread of the antisemitic "Happy Merchant" meme, and in
particular how these fringe communities influence its propagation to more
mainstream communities like Twitter and Reddit. Taken together, our results
provide a data-driven, quantitative framework for understanding online
antisemitism. Our methods serve as a framework to augment current qualitative
efforts by anti-hate groups, providing new insights into the growth and spread
of hate online.Comment: To appear at the 14th International AAAI Conference on Web and Social
Media (ICWSM 2020). Please cite accordingl
The power of indirect social ties
While direct social ties have been intensely studied in the context of
computer-mediated social networks, indirect ties (e.g., friends of friends)
have seen little attention. Yet in real life, we often rely on friends of our
friends for recommendations (of good doctors, good schools, or good
babysitters), for introduction to a new job opportunity, and for many other
occasional needs. In this work we attempt to 1) quantify the strength of
indirect social ties, 2) validate it, and 3) empirically demonstrate its
usefulness for distributed applications on two examples. We quantify social
strength of indirect ties using a(ny) measure of the strength of the direct
ties that connect two people and the intuition provided by the sociology
literature. We validate the proposed metric experimentally by comparing
correlations with other direct social tie evaluators. We show via data-driven
experiments that the proposed metric for social strength can be used
successfully for social applications. Specifically, we show that it alleviates
known problems in friend-to-friend storage systems by addressing two previously
documented shortcomings: reduced set of storage candidates and data
availability correlations. We also show that it can be used for predicting the
effects of a social diffusion with an accuracy of up to 93.5%.Comment: Technical Repor
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
Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter
Over the past few years, online bullying and aggression have become
increasingly prominent, and manifested in many different forms on social media.
However, there is little work analyzing the characteristics of abusive users
and what distinguishes them from typical social media users. In this paper, we
start addressing this gap by analyzing tweets containing a great large amount
of abusiveness. We focus on a Twitter dataset revolving around the Gamergate
controversy, which led to many incidents of cyberbullying and cyberaggression
on various gaming and social media platforms. We study the properties of the
users tweeting about Gamergate, the content they post, and the differences in
their behavior compared to typical Twitter users.
We find that while their tweets are often seemingly about aggressive and
hateful subjects, "Gamergaters" do not exhibit common expressions of online
anger, and in fact primarily differ from typical users in that their tweets are
less joyful. They are also more engaged than typical Twitter users, which is an
indication as to how and why this controversy is still ongoing. Surprisingly,
we find that Gamergaters are less likely to be suspended by Twitter, thus we
analyze their properties to identify differences from typical users and what
may have led to their suspension. We perform an unsupervised machine learning
analysis to detect clusters of users who, though currently active, could be
considered for suspension since they exhibit similar behaviors with suspended
users. Finally, we confirm the usefulness of our analyzed features by emulating
the Twitter suspension mechanism with a supervised learning method, achieving
very good precision and recall.Comment: In 28th ACM Conference on Hypertext and Social Media (ACM HyperText
2017
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