2,210 research outputs found
Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes
Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets
Fame for sale: efficient detection of fake Twitter followers
are those Twitter accounts specifically created to
inflate the number of followers of a target account. Fake followers are
dangerous for the social platform and beyond, since they may alter concepts
like popularity and influence in the Twittersphere - hence impacting on
economy, politics, and society. In this paper, we contribute along different
dimensions. First, we review some of the most relevant existing features and
rules (proposed by Academia and Media) for anomalous Twitter accounts
detection. Second, we create a baseline dataset of verified human and fake
follower accounts. Such baseline dataset is publicly available to the
scientific community. Then, we exploit the baseline dataset to train a set of
machine-learning classifiers built over the reviewed rules and features. Our
results show that most of the rules proposed by Media provide unsatisfactory
performance in revealing fake followers, while features proposed in the past by
Academia for spam detection provide good results. Building on the most
promising features, we revise the classifiers both in terms of reduction of
overfitting and cost for gathering the data needed to compute the features. The
final result is a novel classifier, general enough to thwart
overfitting, lightweight thanks to the usage of the less costly features, and
still able to correctly classify more than 95% of the accounts of the original
training set. We ultimately perform an information fusion-based sensitivity
analysis, to assess the global sensitivity of each of the features employed by
the classifier. The findings reported in this paper, other than being supported
by a thorough experimental methodology and interesting on their own, also pave
the way for further investigation on the novel issue of fake Twitter followers
Characterizing Key Stakeholders in an Online Black-Hat Marketplace
Over the past few years, many black-hat marketplaces have emerged that
facilitate access to reputation manipulation services such as fake Facebook
likes, fraudulent search engine optimization (SEO), or bogus Amazon reviews. In
order to deploy effective technical and legal countermeasures, it is important
to understand how these black-hat marketplaces operate, shedding light on the
services they offer, who is selling, who is buying, what are they buying, who
is more successful, why are they successful, etc. Toward this goal, in this
paper, we present a detailed micro-economic analysis of a popular online
black-hat marketplace, namely, SEOClerks.com. As the site provides
non-anonymized transaction information, we set to analyze selling and buying
behavior of individual users, propose a strategy to identify key users, and
study their tactics as compared to other (non-key) users. We find that key
users: (1) are mostly located in Asian countries, (2) are focused more on
selling black-hat SEO services, (3) tend to list more lower priced services,
and (4) sometimes buy services from other sellers and then sell at higher
prices. Finally, we discuss the implications of our analysis with respect to
devising effective economic and legal intervention strategies against
marketplace operators and key users.Comment: 12th IEEE/APWG Symposium on Electronic Crime Research (eCrime 2017
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