Detecting anomalies in social network data consumption

Akcora, Cüneyt Gürcan; Carminati, Barbara; Ferrari, Elena; Kantarcıoğlu, Murat

doi:10.1007/s13278-014-0231-3

Cited by 12 publications

(8 citation statements)

References 21 publications

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“…Those include Senators and members of the Australian House of Representatives. (ii) A selected set of users is assembled from three distinguished Australian Twitter lists that are relevant [51] is twofold: (i) to proof the efficiency and applicability of the proposed approach which can be used to eliminate spammers and their content and entrench the domain KG with trustworthy facts; (ii) to embed also the content of domain influencers from a dataset of users whose domains of knowledge are not explicitly known.…”

Section: Dataset Acquisitionmentioning

confidence: 99%

“…As mentioned previously our study uses the Twitter graph dataset crawled by Akcora et al [51]. This dataset comprises spammers and other anomalous users.…”

Section: Knowledge Credibility Modulementioning

confidence: 99%

“…Users whose political interest is not explicitly identified were tentatively selected various Australian Twitter's lists established to discuss sports, Information Technology, and other non-politics domains. Finally, we included a subset of users indicated in the Twitter graph dataset collected by Akcora et al[51]. This graph was used in experiments carried out by Akcora et al to discover spammers and other illegitimate accounts.…”

mentioning

confidence: 99%

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Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Abu-Salih

Al-Tawil

Aljarah

et al. 2021

Data Min Knowl Disc

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Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools. Therefore, the application of KGs has extended to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data obtained from heterogeneous resources into a formal KG representation depicted by a domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a credibility module to ensure data quality and trustworthiness. The constructed KG is then embedded in a low-dimension semantically-continuous space using several embedding techniques. The utility of the constructed KG and its embeddings is demonstrated and substantiated on link prediction, clustering, and visualisation tasks.

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Section: Dataset Acquisitionmentioning

confidence: 99%

“…As mentioned previously our study uses the Twitter graph dataset crawled by Akcora et al [51]. This dataset comprises spammers and other anomalous users.…”

Section: Knowledge Credibility Modulementioning

confidence: 99%

mentioning

confidence: 99%

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Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Abu-Salih

Al-Tawil

Aljarah

et al. 2021

Data Min Knowl Disc

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“…Data acquisition is carried out using a PHP script triggered by running a cron job which selects a new user_id and starts collecting historical user information, tweets, replies and the related metadata. The list of Twitterers' user_ids used in the data acquisition phase is extracted from a Twitter graph dataset crawled by Akcora et al [101]. This graph is chosen since it includes the list of users who had less than 5,000 friends in 2013.…”

Section: Case Study On Social Credibility Analysismentioning

confidence: 99%

Credibility Analysis in Social Big Data

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

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The concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of SBD is to provide frameworks and methodologies using which the credibility of online social services users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of social big data. Given the exposed environment's settings and fewer limitations related to online social services, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. This chapter presents an overview of the notion of credibility in the context of SBD. It also list an array of approaches to measure and evaluate the trustworthiness of users and their contents. Finally, a case study is presented that incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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“…Shu et al [7] outline the human cost of fake, false and misleading information on society as a whole, and we begin there by making a strong case for the need to identify and contain misinformation. Shu makes the point that fake news is very hard to detect from the news item itself, and we need to resort to meta-data such as likes and retweets, friends and followers.Further justification for the utility of meta-data can be found in Akcora [2]. This paper focuses on the clustered behavior of the friend and follower network in Twitter.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Social Media Using Recurrent Neural Network

Shah

Goyal

2019

Lecture Notes in Computer Science

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In today's information environment there is an increasing reliance on online and social media in the acquisition, dissemination and consumption of news. Specifically, the utilization of social media platforms such as Facebook and Twitter has increased as a cutting edge medium for breaking news. On the other hand, the low cost, easy access and rapid propagation of news through social media makes the platform more sensitive to fake and anomalous reporting. The propagation of fake and anomalous news is not some benign exercise. The extensive spread of fake news has the potential to do serious and real damage to individuals and society. As a result, the detection of fake news in social media has become a vibrant and important field of research. In this paper, a novel application of machine learning approaches to the detection and classification of fake and anomalous data are considered. An initial clustering step with the K-Nearest Neighbor (KNN) algorithm is proposed before training the result with a Recurrent Neural Network (RNN). The results of a preliminary application of the KNN phase before the RNN phase produces a quantitative and measureable improvement in the detection of outliers, and as such is more effective in detecting anomalies or outliers against the test dataset of 2016 US Presidential Election predictions.

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Detecting anomalies in social network data consumption

Cited by 12 publications

References 21 publications

Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Relational Learning Analysis of Social Politics using Knowledge Graph Embedding

Credibility Analysis in Social Big Data

Anomaly Detection in Social Media Using Recurrent Neural Network

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