A Survey and a Preliminary Evaluation of Low-quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?

  • Júlio Resende Universidade Federal de São João del Rei (UFSJ)
  • Igor Moraes Universidade Federal de São João del Rei (UFSJ)
  • Nícollas Silva Universidade Federal de Minas Gerais (UFMG)
  • Vinícius Durelli Universidade Federal de São João del Rei (UFSJ)
  • Diego Dias Universidade Federal de São João del Rei (UFSJ)
  • Leonardo Rocha Universidade Federal de São João del Rei (UFSJ)


Online social networks have gone mainstream: millions of users have come to rely on the wide range of services provided by social networks. However, the ease use of social networks for communicating information also makes them particularly vulnerable to social spammers, i.e., ill-intentioned users whose main purpose is to degrade the information quality of social networks through the proliferation of different types of malicious data (e.g., social spam, malware downloads, and phishing) that are collectively called low-quality content or spams. Since Twitter is also rife with low-quality content, several researchers have devised various low-quality detection strategies that inspect tweets for the existence of spam contents. We carried out a literature survey of these low-quality detection strategies, examining which strategies are still applicable in the current scenario – taken into account that Twitter has undergone a lot of changes in the last few years. To gather some evidence of the usefulness of the attributes used by the low-quality detection strategies, we carried out a preliminary evaluation of these attributes.

Palavras-chave: Spam Detection, Data Mining, Machine Learning


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RESENDE, Júlio; MORAES, Igor; SILVA, Nícollas; DURELLI, Vinícius; DIAS, Diego; ROCHA, Leonardo. A Survey and a Preliminary Evaluation of Low-quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE) , 2019, Fortaleza. Anais do VII Symposium on Knowledge Discovery, Mining and Learning. Porto Alegre: Sociedade Brasileira de Computação, nov. 2019 . p. 17-24. DOI: https://doi.org/10.5753/kdmile.2019.8784.