Twitter fake account detection

Erşahin, Buket; Aktaş, Özlem; Kılınç, Deniz; Akyol, C. Kardelen

doi:10.1109/ubmk.2017.8093420

Cited by 78 publications

(40 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To develop a fake account detection system, a new offline NB model from Sparks MLlib was trained and tested using TwFakeUsr data set. The accuracy and F-measure values were obtained 89.16% and 88.65%, respectively, which were similar to results in the work of Erşahin et al 12 The real-time performance of the system was also tested during the selection of 2000 tweets. The system classified 38 accounts as fake and filtered out the corresponding bogus tweets.…”

Section: Impact Of Real-time Fake Account Detection Systemsupporting

confidence: 85%

“…A Spark cluster generally is composed of a cluster manager (master) and worker nodes. On the other side, NB model can be easily parallelized as it requires only one pass over data set . In order to evaluate the cluster effect of Spark, experiments were performed using four different clustering configurations, ie, (i) one master node, (ii) one master node + one worker node servers, (iii) one master node + two worker node servers, and (iv) one master node + four worker node servers.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, content‐based approaches make use of the properties of a posted tweet that includes the count of mentions, count of hashtags, links, trending topics, and duplicate tweets . Hybrid methods uses the features of both user‐based and content‐based methods …”

Section: Related Workmentioning

confidence: 99%

“…First, the owner of a Tweet is classified as real or fake using Twitter fake account detection service in real time. Moreover, 16 attributes ( description , followers_count , friends_count , statuses_count , etc) of a tweet are extracted and then tested utilizing a NB model that is trained with a data set named TwFakeUsr . If owner of the tweet is classified as fake, then the corresponding tweet message is discarded.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Kılınç

2019

Softw Pract Exp

View full text Add to dashboard Cite

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

show abstract

Section: Impact Of Real-time Fake Account Detection Systemsupporting

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Kılınç

2019

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…The [25] show the way to detect the profiles in twitter if they fake profile or normal one by using technology called (Entropy Minimization Discretization (EMD)) on numerical features and analyzed the results of the Naïve Bayes algorithm).…”

Section: Methods Used For Fake Profile Detectionmentioning

confidence: 99%

Fake Profiles Types of Online Social Networks: A Survey

Kareem¹,

Bhaya²

2018

IJET

View full text Add to dashboard Cite

Today, OSNs (Online Social Networks) considered the most platforms common on the Internet. It plays a substantial role for users of the internet to hold out their everyday actions such as news reading, content sharing, product reviews, messages posting, and eve nts discussing etc. Unfortunately, on the OSNs some new attacks have been recognized. Different types of spammers are existing in these OSNs. These cyber-criminals containing online fraudsters, sexual predators, catfishes, social bots, and advertising campaigners etc. OSNs abuse in different ways especially by creating fake profiles to carry out scams and spread their content. The identities of all these malicious are so damaging to the service providers and the users. From the opinion of OSNs service providers, the loss of bandwidth moreover the overall reputation of the network is affected by fake profiles. Thus, needing more complex automated methods, and tremendous effort manpower to discover and stopping these harmful users. This paper explains different kinds of OSNs risk generators such as cloned profiles, compromised profiles, and online bots (spam-bots, chat-bots, and social-bots). In addition, it presents several classifications of features that have been used for training classifiers in order to discover fake profiles. We try to show different ways that used to detect every kind of these malicious profiles. Also, this paper trying to show what is the dangerous type of profile attacks and the most popular in OSNs.

show abstract

A Feature Based Approach on Behavior Analysis of the Users on Twitter: A Case Study of AusOpen Tennis Championship

Shoeibi

Mateos

Rivas

et al. 2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Due to the advancement of technology, and the promotion of smartphones, using social media got more and more popular. Nowadays, it has become an undeniable part of people's lives. So, they will create a flow of information by the content they share every single moment. Analyzing this information helps us to have a better understanding of users, their needs, their tendencies and classify them into different groups based on their behavior. These behaviors are various and due to some extracted features, it is possible to categorize the users into different categories. In this paper, we are going to focus on Twitter users and the AusOpen Tennis championship event as a case study. We define the attributions describing each class and then extract data and identify features that are more correlated to each type of user and then label user type based on the reasoning model. The results contain 4 groups of users; Verified accounts, Influencers, Regular profiles, and Fake profiles.

show abstract

Twitter fake account detection

Cited by 78 publications

References 6 publications

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Fake Profiles Types of Online Social Networks: A Survey

A Feature Based Approach on Behavior Analysis of the Users on Twitter: A Case Study of AusOpen Tennis Championship

Contact Info

Product

Resources

About