2020
DOI: 10.1007/s42452-020-03374-x
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Context-dependent model for spam detection on social networks

Abstract: Social media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independ… Show more

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Cited by 13 publications
(4 citation statements)
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“…Rumor is information initiated by a potentially untrustworthy person and circulated among users before it can be verified to be true, false, or unconfirmed [21]. Spam refers to any irrelevant or unsolicited messages sent by individuals or groups on social media, such as advertisements, malicious links, or content of poor quality [22]. Misinformation is false information that is spread unintentionally, for example, by mistake or updating specific knowledge without intentionally misleading [23].…”
Section: A Fake News Definitionmentioning
confidence: 99%
“…Rumor is information initiated by a potentially untrustworthy person and circulated among users before it can be verified to be true, false, or unconfirmed [21]. Spam refers to any irrelevant or unsolicited messages sent by individuals or groups on social media, such as advertisements, malicious links, or content of poor quality [22]. Misinformation is false information that is spread unintentionally, for example, by mistake or updating specific knowledge without intentionally misleading [23].…”
Section: A Fake News Definitionmentioning
confidence: 99%
“…Ghanem, Erbay, and Bakour [9] tackle the issue of spam on social networks by proposing a RoBERTa-based bi-directional Recurrent Neural Network for spam detection. Their study demonstrates superior performance, outperforming common transformer-based models on benchmark datasets from Twitter, YouTube, and SMS.…”
Section: Related Workmentioning
confidence: 99%
“…Spam text detection on social media is mostly done on Twitter [11], [13], [14], [15]. Twitter has a structure that is not in the posts and comments pair structure.…”
Section: Related Workmentioning
confidence: 99%
“…This dataset is useful for NLP research because most researchers discard emojis in their classification techniques. Some examples are news article classification [10], Twitter without emoji [11], spam comments from the blog [12], Twitter (removed emoji) [13], SMS and Twitter without emoji [14], Twitter without emoji [15], Youtube comment without emoji [16], video spam comment without emoji [17], Youtube comment without emoji [18]. The emoji is essential because most social media users use emojis to express their feelings, such as to support/deny, show sympathy, joy, sadness, and anger.…”
Section: Introductionmentioning
confidence: 99%