5,334 research outputs found
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Increasing evidence suggests that a growing amount of social media content is
generated by autonomous entities known as social bots. In this work we present
a framework to detect such entities on Twitter. We leverage more than a
thousand features extracted from public data and meta-data about users:
friends, tweet content and sentiment, network patterns, and activity time
series. We benchmark the classification framework by using a publicly available
dataset of Twitter bots. This training data is enriched by a manually annotated
collection of active Twitter users that include both humans and bots of varying
sophistication. Our models yield high accuracy and agreement with each other
and can detect bots of different nature. Our estimates suggest that between 9%
and 15% of active Twitter accounts are bots. Characterizing ties among
accounts, we observe that simple bots tend to interact with bots that exhibit
more human-like behaviors. Analysis of content flows reveals retweet and
mention strategies adopted by bots to interact with different target groups.
Using clustering analysis, we characterize several subclasses of accounts,
including spammers, self promoters, and accounts that post content from
connected applications.Comment: Accepted paper for ICWSM'17, 10 pages, 8 figures, 1 tabl
Social Turing Tests: Crowdsourcing Sybil Detection
As popular tools for spreading spam and malware, Sybils (or fake accounts)
pose a serious threat to online communities such as Online Social Networks
(OSNs). Today, sophisticated attackers are creating realistic Sybils that
effectively befriend legitimate users, rendering most automated Sybil detection
techniques ineffective. In this paper, we explore the feasibility of a
crowdsourced Sybil detection system for OSNs. We conduct a large user study on
the ability of humans to detect today's Sybil accounts, using a large corpus of
ground-truth Sybil accounts from the Facebook and Renren networks. We analyze
detection accuracy by both "experts" and "turkers" under a variety of
conditions, and find that while turkers vary significantly in their
effectiveness, experts consistently produce near-optimal results. We use these
results to drive the design of a multi-tier crowdsourcing Sybil detection
system. Using our user study data, we show that this system is scalable, and
can be highly effective either as a standalone system or as a complementary
technique to current tools
A Green Criminological Exploration of Illegal Wildlife Trade in Vietnam
Illegal wildlife trade is a pervasive and destructive crime that is contributing to biodiversity loss and species extinction around the globe. This is particularly true in Vietnam where, it is proposed, the convergence of four factors creates the conditions for the illegal wildlife trade to flourish. The human-centered approach to Vietnam’s diverse ecosystem, historic consumption of wildlife, rapidly developing economy, and embryonic environmental legislation has resulted in the continued degradation of a unique and important environment. Furthermore, until recently criminological research of such green crimes has either been lacking or equally human-centered. This article details the nature and extent of wildlife trafficking in Vietnam and introduces to this context an expanded notion of harm, including the environment and other species, of a green criminological perspective to this exploration. The aim is that, by proposing a new framework in which to evaluate the illegal wildlife trade and other green crimes in Vietnam, new and innovative strategies addressing the convergent factors might be developed that will aid in stopping the illegal wildlife trade and other green crimes
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