765 research outputs found
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Social spam produces a great amount of noise on social media services such as
Twitter, which reduces the signal-to-noise ratio that both end users and data
mining applications observe. Existing techniques on social spam detection have
focused primarily on the identification of spam accounts by using extensive
historical and network-based data. In this paper we focus on the detection of
spam tweets, which optimises the amount of data that needs to be gathered by
relying only on tweet-inherent features. This enables the application of the
spam detection system to a large set of tweets in a timely fashion, potentially
applicable in a real-time or near real-time setting. Using two large
hand-labelled datasets of tweets containing spam, we study the suitability of
five classification algorithms and four different feature sets to the social
spam detection task. Our results show that, by using the limited set of
features readily available in a tweet, we can achieve encouraging results which
are competitive when compared against existing spammer detection systems that
make use of additional, costly user features. Our study is the first that
attempts at generalising conclusions on the optimal classifiers and sets of
features for social spam detection over different datasets
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Recent studies in social media spam and automation provide anecdotal
argumentation of the rise of a new generation of spambots, so-called social
spambots. Here, for the first time, we extensively study this novel phenomenon
on Twitter and we provide quantitative evidence that a paradigm-shift exists in
spambot design. First, we measure current Twitter's capabilities of detecting
the new social spambots. Later, we assess the human performance in
discriminating between genuine accounts, social spambots, and traditional
spambots. Then, we benchmark several state-of-the-art techniques proposed by
the academic literature. Results show that neither Twitter, nor humans, nor
cutting-edge applications are currently capable of accurately detecting the new
social spambots. Our results call for new approaches capable of turning the
tide in the fight against this raising phenomenon. We conclude by reviewing the
latest literature on spambots detection and we highlight an emerging common
research trend based on the analysis of collective behaviors. Insights derived
from both our extensive experimental campaign and survey shed light on the most
promising directions of research and lay the foundations for the arms race
against the novel social spambots. Finally, to foster research on this novel
phenomenon, we make publicly available to the scientific community all the
datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science
Track, Perth, Australia, 3-7 April, 2017
Seminar Users in the Arabic Twitter Sphere
We introduce the notion of "seminar users", who are social media users
engaged in propaganda in support of a political entity. We develop a framework
that can identify such users with 84.4% precision and 76.1% recall. While our
dataset is from the Arab region, omitting language-specific features has only a
minor impact on classification performance, and thus, our approach could work
for detecting seminar users in other parts of the world and in other languages.
We further explored a controversial political topic to observe the prevalence
and potential potency of such users. In our case study, we found that 25% of
the users engaged in the topic are in fact seminar users and their tweets make
nearly a third of the on-topic tweets. Moreover, they are often successful in
affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201
Contextual Outlier Interpretation
Outlier detection plays an essential role in many data-driven applications to
identify isolated instances that are different from the majority. While many
statistical learning and data mining techniques have been used for developing
more effective outlier detection algorithms, the interpretation of detected
outliers does not receive much attention. Interpretation is becoming
increasingly important to help people trust and evaluate the developed models
through providing intrinsic reasons why the certain outliers are chosen. It is
difficult, if not impossible, to simply apply feature selection for explaining
outliers due to the distinct characteristics of various detection models,
complicated structures of data in certain applications, and imbalanced
distribution of outliers and normal instances. In addition, the role of
contrastive contexts where outliers locate, as well as the relation between
outliers and contexts, are usually overlooked in interpretation. To tackle the
issues above, in this paper, we propose a novel Contextual Outlier
INterpretation (COIN) method to explain the abnormality of existing outliers
spotted by detectors. The interpretability for an outlier is achieved from
three aspects: outlierness score, attributes that contribute to the
abnormality, and contextual description of its neighborhoods. Experimental
results on various types of datasets demonstrate the flexibility and
effectiveness of the proposed framework compared with existing interpretation
approaches
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