Data Quality Challenges in Social Spam Research

El-Mawass, Nour; Alaboodi, Saad Saleh

doi:10.1145/3090057

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…In response to the web-spamming challenge [12], different researchers proposed various methods for identifying content-based spamdexing. A group of researchers [3] suggested a few critical content features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…ese models are applied to a web page's content for ranking the web page, so content-based web spamming is very popular among web spammers. To manipulate the spam web pages' content, web spammers utilise vulnerabilities of these models [12]. For example, they might use famous keywords many times on a spam web page to increase the keywords' frequencies, copy legitimate website's content, produce the content for spam web pages using machine-generated techniques, and add dictionary's words on a spam web page, giving those words the colour of the background so that these words cannot be seen on the spam web page by the user and are only visible to search engine spiders.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Framework for Content‐ and Link‐Based Web‐Spam Detection: A Combined Approach

et al. 2021

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In this modern era, people utilise the web to share information and to deliver services and products. The information seekers use different search engines (SEs) such as Google, Bing, and Yahoo as tools to search for products, services, and information. However, web spamming is one of the most significant issues encountered by SEs because it dramatically affects the quality of SE results. Web spamming’s economic impact is enormous because web spammers index massive free advertising data on SEs to increase the volume of web traffic on a targeted website. Spammers trick an SE into ranking irrelevant web pages higher than relevant web pages in the search engine results pages (SERPs) using different web-spamming techniques. Consequently, these high-ranked unrelated web pages contain insufficient or inappropriate information for the user. To detect the spam web pages, several researchers from industry and academia are working. No efficient technique that is capable of catching all spam web pages on the World Wide Web (WWW) has been presented yet. This research is an attempt to propose an improved framework for content- and link-based web-spam identification. The framework uses stopwords, keywords’ frequency, part of speech (POS) ratio, spam keywords database, and copied-content algorithms for content-based web-spam detection. For link-based web-spam detection, we initially exposed the relationship network behind the link-based web spamming and then used the paid-link database, neighbour pages, spam signals, and link-farm algorithms. Finally, we combined all the content- and link-based spam identification algorithms to identify both types of spam. To conduct experiments and to obtain threshold values, WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets were used. A promising F-measure of 79.6% with 81.2% precision shows the applicability and effectiveness of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Framework for Content‐ and Link‐Based Web‐Spam Detection: A Combined Approach

et al. 2021

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“…In the most recent years, several content-based spamdexing identification methods are proposed by the researchers during the spamdexing challenge [13]. Ntoulas et.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This spamdexing technique is popular among the web spammers because of several SEs are using the information retrieval models (IRM) for instance, statistical language model [10], vector space model [11], BM25 [12] and probabilistic model which are applied to the web page's content for ranking the web page. Spammers try to utilize the vulnerabilities of these models for manipulating the content of target web page [13]. For instance, using important keywords several times on a target web page and increasing the keywords frequencies, copying the content from various good web pages, using the machine-generated content on target web page, and putting all dictionary words on the page and then changing the color of text similar to the background color so users can not see the dictionary words on target page and only visible to SEs spiders are some methods which web spammers are using for getting higher page rank in the SERPs [14].…”

Section: Introductionmentioning

confidence: 99%

An Improved Framework for Content-based Spamdexing Detection

Shahzad¹,

Mahdin²,

Mohd³

2020

IJACSA

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To the modern Search Engines (SEs), one of the biggest threats to be considered is spamdexing. Nowadays spammers are using a wide range of techniques for content generation, they are using content spam to fill the Search Engine Result Pages (SERPs) with low-quality web pages. Generally, spam web pages are insufficient, irrelevant and improper results for users. Many researchers from academia and industry are working on spamdexing to identify the spam web pages. However, so far not even a single universally efficient method is developed for identification of all spam web pages. We believe that for tackling the content spam there must be improved methods. This article is an attempt in that direction, where a framework has been proposed for spam web pages identification. The framework uses Stop words, Keywords Density, Spam Keywords Database, Part of Speech (POS) ratio, and Copied Content algorithms. For conducting the experiments and obtaining threshold values WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets have been used. An excellent and promising F-measure of 77.38% illustrates the effectiveness and applicability of proposed method.

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“…Twitter has defined spamming behavior and the rules of tweet freedom [2,10,11]. We established our definition of a spam tweet.…”

Section: Motivationmentioning

confidence: 99%

Predicting Rogue Content and Arabic Spammers on Twitter

Alharbi

Aljaedi

2019

Future Internet

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Twitter is one of the most popular online social networks for spreading propaganda and words in the Arab region. Spammers are now creating rogue accounts to distribute adult content through Arabic tweets that Arabic norms and cultures prohibit. Arab governments are facing a huge challenge in the detection of these accounts. Researchers have extensively studied English spam on online social networks, while to date, social network spam in other languages has been completely ignored. In our previous study, we estimated that rogue and spam content accounted for approximately three quarters of all content with Arabic trending hashtags in Saudi Arabia. This alarming rate, supported by autonomous concurrent estimates, highlights the urgent need to develop adaptive spam detection methods. In this work, we collected a pure data set from spam accounts producing Arabic tweets. We applied lightweight feature engineering based on rogue content and user profiles. The 47 generated features were analyzed, and the best features were selected. Our performance results show that the random forest classification algorithm with 16 features performs best, with accuracy rates greater than 90%.

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Data Quality Challenges in Social Spam Research

Cited by 6 publications

References 9 publications

An Improved Framework for Content‐ and Link‐Based Web‐Spam Detection: A Combined Approach

An Improved Framework for Content‐ and Link‐Based Web‐Spam Detection: A Combined Approach

An Improved Framework for Content-based Spamdexing Detection

Predicting Rogue Content and Arabic Spammers on Twitter

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