Community detection in social networks using content and link analysis

Kakisim, Arzu Gorgulu; Soğukpınar, İbrahim

doi:10.1109/siu.2015.7130135

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…However, if a reviewer reviews a product soon after it is launched, then review will more likely be a spam. Therefore, we compute the total time ( ) between the product listing time and the review time using equation (4). As the frequency of review rating is a a boolean feature, so if a product is reviewed within three days, then we classify it as a spam.…”

Section: Features Engineeringmentioning

confidence: 99%

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Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

2018

KSII TIIS

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Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

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Section: Features Engineeringmentioning

confidence: 99%

“…Spam can be categorized into numerous types [4,5], but the most common are email spam, SMS spam, web spam, and review spam. Mail spam is related to unwanted electronic messages [6,7].…”

Section: Introductionmentioning

confidence: 99%

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

2018

KSII TIIS

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“…Analysis of the ST (social topic) model, in which the FT and ST models are independent of LDA models [16]. Natarajan et al [17] used link community as the starting point to establish a link-content model that uses linkcontent as the semantic analysis object [18,19].…”

Section: Related Workmentioning

confidence: 99%

Tibetan Weibo User Group Division Based on Semantic Information in the Era of Big Data

Qiu

et al. 2018

Complexity

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In the era of big data, group division in online social network analysis is a basic task. It can be divided into the group division based on static relationship and the group division based on dynamic relationship. Compared with the static group division, users express their semantic information in all kinds of social network behaviors, and they tend to interact with other users who have the same idea and attitude; this is how different groups are formed. In this paper, aimed at the issue that some Tibetan users use Chinese to publish microblogs on social platforms, a group division method based on semantic information of Tibetan users under the big data environment is proposed. When dividing a large number of Tibetan user groups in a social network, a large amount of semantic information of Tibetan users in the social network is first analyzed. Then, based on the semantic similarity between users, we aggregate the Tibetan users with high similarities into one group, thus achieving the final group division. The experimental results illustrate the effectiveness of the method of analyzing Tibetan user semantic information in the context of big data for group partitioning.

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Öznitelikli Ağlarda Topluluk Arama

Kağnici¹,

Kakisim²

2021

European Journal of Science and Technology

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ÖzSosyal ağların yoğun bir şekilde kullanımıyla birlikte, bir dizi sosyal nesnelerden ve nesnelere ait sosyal etkileşimlerden oluşan geniş ölçekli karmaşık ağ yapıları ortaya çıkmaktadır. Son zamanlarda, bu büyük ağ yapılarını analiz etmek için ağlardaki anlamlı grupları diğer bir deyişle toplulukları arama yaklaşımları önerilmektedir. Topluluk arama, sorgu nesneleri kullanarak, karmaşık ağ yapılarından sorgu nesneleri ile ilişkili, belli özellikler açısından birbirleri ile benzerlik gösteren ve birbirleri ile yoğun etkileşimde olan toplulukları ortaya çıkarma problemidir. Öznitelikli ağlar, düğümleri ağ nesnelerini, kenarları bu nesneler arasındaki ilişkileri temsil eden graf yapılarından ve ağdaki düğümleri karakterize eden içerik verisi diğer bir deyişle özniteliklerden oluşmaktadır. Geleneksel topluluk arama yaklaşımları, düğümlere ait öznitelik verisini göz ardı ederek, sadece ağ topolojisine odaklanmaktadır. Bu çalışmada, öznitelikli ağlarda topluluk arama problemi için öznitelik verisini ağ topolojisine entegre etme fikrine dayanan yeni bir yaklaşım öneriyoruz. Önerilen yöntem, her düğümün yalnızca kendi öznitelikleriyle bağlı olduğu yeni bir graf temsili oluşturarak, ağ topolojisine içeriksel bir boyut eklemektedir. Bu çalışmada, mevcut topluluk arama algoritmalarını öznitelikli ağlar üzerinde uygulanabilir hale getirmek, bu algoritmaların başarısını artırmak ve öznitelik verisini sorgu verisi olarak kullanılabilir hale getirmek amaçlanmaktadır. Gerçek dünya veri setleri üzerinden elde edilen deneysel sonuçlar, önerilen yöntemin temel yöntemler ile karşılaştırıldığında daha yüksek doğruluk değerleri elde ettiğini göstermektedir.

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Community detection in social networks using content and link analysis

Cited by 3 publications

References 17 publications

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

Tibetan Weibo User Group Division Based on Semantic Information in the Era of Big Data

Öznitelikli Ağlarda Topluluk Arama

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