16,591 research outputs found
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
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
Deriving Verb Predicates By Clustering Verbs with Arguments
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora such as Twitter. We present a method for clustering the
outputs of VerbKB: verbs with their multiple argument types, e.g.
"marry(person, person)", "feel(person, emotion)." We make use of a novel
low-dimensional embedding of verbs and their arguments to produce high quality
clusters in which the same verb can be in different clusters depending on its
argument type. The resulting verb clusters do a better job than hand-built
clusters of predicting sarcasm, sentiment, and locus of control in tweets
Jumping Finite Automata for Tweet Comprehension
Every day, over one billion social media text messages are generated worldwide, which provides abundant information that can lead to improvements in lives of people through evidence-based decision making. Twitter is rich in such data but there are a number of technical challenges in comprehending tweets including ambiguity of the language used in tweets which is exacerbated in under resourced languages. This paper presents an approach based on Jumping Finite Automata for automatic comprehension of tweets. We construct a WordNet for the language of Kenya (WoLK) based on analysis of tweet structure, formalize the space of tweet variation and abstract the space on a Finite Automata. In addition, we present a software tool called Automata-Aided Tweet Comprehension (ATC) tool that takes raw tweets as input, preprocesses, recognise the syntax and extracts semantic information to 86% success rate
- …