Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks

Abulaish, Muhammad; Bhat, Sajid Yousuf

doi:10.1515/fcds-2015-0006

Cited by 12 publications

(14 citation statements)

References 28 publications

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“…Fictitious reviews were more exaggerated than authentic ones—a finding consistent with prior studies (DePaulo et al, ; Yoo & Gretzel, ). Even though spammers are growing smarter (Abulaish & Bhat, ), they are not adept enough to blur the lines between authentic and fictitious reviews based on exaggeration. Based on negligence, fictitious reviews were richer in first‐person singular words than authentic entries.…”

Section: Discussionmentioning

confidence: 99%

“…Authentic reviews are generally expected to be less exaggerated than fictitious ones (Maurer & Schaich, 2011). However, recent literature finds evidence of spammers becoming smarter to blur the lines between authentic and fictitious entries (Abulaish & Bhat, 2015). To deliberately mimic authentic entries, fictitious reviews might not be overly exaggerated.…”

Section: Exaggerationmentioning

confidence: 99%

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Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity

Banerjee

Chua

Kim

2017

Asso for Info Science & Tech

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This article uses linguistic analysis to help users discern the authenticity of online reviews. Two related studies were conducted using hotel reviews as the test case for investigation. The first study analyzed 1,800 authentic and fictitious reviews based on the linguistic cues of comprehensibility, specificity, exaggeration, and negligence. The analysis involved classification algorithms followed by feature selection and statistical tests. A filtered set of variables that helped discern review authenticity was identified. The second study incorporated these variables to develop a guideline that aimed to inform humans how to distinguish between authentic and fictitious reviews. The guideline was used as an intervention in an experimental setup that involved 240 participants. The intervention improved human ability to identify fictitious reviews amid authentic ones.

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Section: Discussionmentioning

confidence: 99%

Section: Exaggerationmentioning

confidence: 99%

Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity

Banerjee

Chua

Kim

2017

Asso for Info Science & Tech

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“…The authors of these tweets share their opinions on real life events and discuss different issues of the society. Recently, a large number of literatures have proposed methods to analyze social network data, especially the Twitter data, for various purposes [2,3,4,5,6,7]. In [8], the authors used the predictive power of social media data to identify the conflicting in US election 2010 using sentiment analysis methods.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Tweets Clustering and Analysis

Jahiruddin¹

2018

IJCSE

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Abstract-Event-based social network analysis is an important task for monitoring the potential threats to the security of a nation and identifying various trends that are popular among the people. In this paper, we propose content-based tweets clustering and analysis method, which aims to cluster tweets based on the events represented by them. The proposed method starts with modeling tweets into a similarity graph (aka social network), in which each node represent a tweet and an edge connecting a node-pair represents the degree of similarity between the tweets represented by them. For social graph generation, each node is represented as a feature vector which is generated using Latent Dirichlet Allocation (LDA) from the respective tweet and edge weight is determined as the similarity between the nodes. Finally, the generated social graph is partitioned into a number of clusters (sub-graphs) using Markov Clustering (MCL) algorithm, where each sub-graph represent an event. We have generated a data set of 5000 tweets related to four different events -Uri attacks, Delhi assembly election, Union budget 2015, and Israel-Gaza conflict to evaluate the proposed method. The experimental results are encouraging, showing high accuracy in grouping tweets based on their contents. We have also performed a comparative analysis of the Cosine similarity and Euclidean distance based similarity graph generation, and it is found that the Cosine similarity yields better results than the Euclidian distance measure.

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“…Since graph is a popular data structure to model structural as well as contextual relationships of data objects, recently graph data clustering is considered as one of the interesting and challenging research problems [16,22], and it aims to decompose a large graph into di erent densely connected components. Some of the applications of the graph clustering is community detection in online social networks [5,7,8], social bot detection [10,11], spammer detection [1,3,6,9], functional relation identi cation in large protein-protein interaction networks, and so on. Generally graph clustering methods are based on the concept of normalized cut, structural density, or modularity [16,22].…”

Section: Related Workmentioning

confidence: 99%

“…an one dimensional vector. In case, there is no direct edge between a pair of vertices (e.g., 1 and 3 ), the corresponding edge vector is a zero vector. If we simply calculate the Euclidean distance between the vertex-pairs, then the distance between the vertex-pairs ( 1 , 2 ), ( 2 , 3 ), ( 3 , 4 ) and ( 4 , 1 ) is same (10 unit), whereas the distance calculated by the proposed MAGDist di ers, based on the weight of the edges between them.…”

mentioning

confidence: 99%

A Novel Weighted Distance Measure for Multi-Attributed Graph

Abulaish

Jahiruddin

2017

Proceedings of the 10th Annual ACM India Compute Conference

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Due to exponential growth of complex data, graph structure has become increasingly important to model various entities and their interactions, with many interesting applications including, bioinformatics, social network analysis, etc. Depending on the complexity of the data, the underlying graph model can be a simple directed/undirected and/or weighted/un-weighted graph to a complex graph (aka multi-attributed graph) where vertices and edges are labelled with multi-dimensional vectors. In this paper, we present a novel weighted distance measure based on weighted Euclidean norm which is de ned as a function of both vertex and edge attributes, and it can be used for various graph analysis tasks including classi cation and cluster analysis. The proposed distance measure has exibility to increase/decrease the weightage of edge labels while calculating the distance between vertex-pairs. We have also proposed a MAGDist algorithm, which reads multi-attributed graph stored in CSV les containing the list of vertex vectors and edge vectors, and calculates the distance between each vertex-pair using the proposed weighted distance measure. Finally, we have proposed a multi-attributed similarity graph generation algorithm, MAGSim, which reads the output of MAGDist algorithm and generates a similarity graph that can be analysed using classi cation and clustering algorithms. The signi cance and accuracy of the proposed distance measure and algorithms is evaluated on Iris and Twitter data sets, and it is found that the similarity graph generated by our proposed method yields better clustering results than the existing similarity graph generation methods.

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Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks

Cited by 12 publications

References 28 publications

Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity

Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity

Content-Based Tweets Clustering and Analysis

A Novel Weighted Distance Measure for Multi-Attributed Graph

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