An ensemble approach for spam detection in Arabic opinion texts

Saeed, Radwa M.K.; Rady, Sherine; Gharib, Tarek F.

doi:10.1016/j.jksuci.2019.10.002

Cited by 38 publications

(20 citation statements)

References 7 publications

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“…While there are different techniques in NLP, n-grams are the most commonly used feature extraction method in text classification [70] and the base of language modeling [71]. N-grams' key advantages are their language independence and simplicity [72].…”

Section: Discussionmentioning

confidence: 99%

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

2021

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Focusing on public-centered, social-mediated crisis communication, the current exploratory study drew on situational crisis communication theory to formulate a comprehensive view of consumer reactions to crisis. Data mining and automated content analysis techniques were utilized to analyze social media posts by the public during a crisis in the cereals industry. Two path analyses showed that: (a) crisis-related social media posts tended to skip over competitor brand products, followed by two major reaction paths—(1) a rational path based on guilt attribution that justifies implications for the company and (2) an emotional path associated with public distrust; and (b) public self-blame spilled over to other stakeholders such as the government and economic system. The results give voice to issues that concern the public during crises, both as individuals and as a community. They highlight the fact that sustainable crisis management should involve additional stakeholders. Conclusions and implications for society and practice are suggested.

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Section: Discussionmentioning

confidence: 99%

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

2021

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“…For spam detection in Arabic opinion texts an ensemble approach has been proposed by Saeed et al (2019). A stacking ensemble classifier that combines a k-means classifier with a rule-based classifier outperforms the rest of the examined approaches.…”

Section: Non-english and Multilanguage Researchmentioning

confidence: 99%

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

et al. 2021

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Automatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.

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“…Stacking model ensembles several base-predictors machine learning models using the stacking method. It was initially proposed by Ting & Witten (1997) and used in several studies for classification tasks like malware detection (Gupta & Rani, 2020), credit card fraud detection (Olowookere & Adewale, 2020), and spam detection (Saeed, Rady & Gharib, 2019). It can perform both classification and regression on data.…”

Section: Stackingmentioning

confidence: 99%

“…In soft-voting, the final class is a class with the highest probability averaged over the individual predictors (González et al, 2020). Voting method have used in several classification tasks like fake news detection (Kaur, Kumar & Kumaraguru, 2020), spam detection (Saeed, Rady & Gharib, 2019), and slope stability analysis (Pham et al, 2021).…”

Section: Votingmentioning

confidence: 99%

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Akhter

Zheng

Afzal

et al. 2021

PeerJ Computer Science

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The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors’ predictions to improve the fake news detection system’s overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.

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An ensemble approach for spam detection in Arabic opinion texts

Cited by 38 publications

References 7 publications

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

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