Early detection of deception and aggressiveness using profile-based representations

Villatoro-Tello, Esaú; Garza, Sara E.; López-Monroy, Adrián Pastor; Montes-y-Gómez, Manuel; Villaseñor-Pineda, Luis

doi:10.1016/j.eswa.2017.07.040

Cited by 33 publications

(29 citation statements)

References 24 publications

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“…The benefits of our approach are that it is model independent, easy to implement, and computes lower dimensional and less-sparse representations than traditional BoW. More important, our method improves over state of the art methods, outperforming the methods in (Errecalde et al, 2017;Escalante et al, 2017) that in turn, outperform that in (Dulac-Arnold et al, 2011;Escalante et al, 2016).…”

Section: Related Workmentioning

confidence: 84%

“…Furthermore, MulR representation obtains better performance than the work in (Escalante et al, 2017), which consists in averaging the PBRs (same that Avg-PBR) and is the state-of-the-art in early SPD. Note that different than (Escalante et al, 2017), the proposed MulR significantly improves even after reading 40% of the information. The experimental results in Table 3 also show the Table 3: F 1 results for the chunk by chunk evaluation of different approaches in Sexual Predator Detection.…”

Section: Sexual Predators Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Early Text Classification Using Multi-Resolution Concept Representations

Monroy¹,

González

Montes

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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This paper proposes a novel document representation, called Multi-Resolution Representation (MulR), to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as little evidence as possible and with as much anticipation as possible. MulR allows us to generate multiple "views" of the text. These views capture different semantic meanings for words and documents at different levels of granularity, which is very useful in early scenarios to model the variable amounts of evidence. The experimental evaluation shows that MulR using low resolution is better suited for modeling short documents (very early stages), whereas large documents (medium/late stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.

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Section: Related Workmentioning

confidence: 84%

Section: Sexual Predators Detectionmentioning

confidence: 99%

Early Text Classification Using Multi-Resolution Concept Representations

Monroy¹,

González

Montes

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

Self Cite

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“…For instance, some works have addressed early text classification by using diverse techniques like modifications of Naive Bayes (Escalante et al, 2016), profile-based representations (Escalante et al, 2017), and Multi-Resolution Concept Representations (López-Monroy et al, 2018). Those approaches have focused on quantifying prediction performance of the classifiers when using partial information in documents, that is, by considering how well they behave when incremental percentages of documents are provided to the classifier.…”

Section: Analysis Of Sequential Data: Early Classificationmentioning

confidence: 99%

“…However, the most important (and interesting) cases are when the delay in that decision could also have negative or risky implications. This scenario, known as "early risk detection" have gained increasing interest in recent years with potential applications in rumor detection (Ma et al, 2015(Ma et al, , 2016Kwon et al, 2017), sexual predator detection and aggressive text identification (Escalante et al, 2017), depression detection (Losada et al, 2017;Losada & Crestani, 2016) or terrorism detection (Iskandar, 2017).…”

Section: Analysis Of Sequential Data: Early Classificationmentioning

confidence: 99%

A text classification framework for simple and effective early depression detection over social media streams

Burdisso

Errecalde

Montes-y-Gómez

2019

Expert Systems with Applications

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137

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With the rise of the Internet, there is a growing need to build intelligent systems that are capable of efficiently dealing with early risk detection (ERD) problems on social media, such as early depression detection, early rumor detection or identification of sexual predators. These systems, nowadays mostly based on machine learning techniques, must be able to deal with data streams since users provide their data over time. In addition, these systems must be able to decide when the processed data is sufficient to actually classify users. Moreover, since ERD tasks involve risky decisions by which people's lives could be affected, such systems must also be able to justify their decisions. However, most standard and state-of-the-art supervised machine learning models (such as SVM, MNB, Neural Networks, etc.) are not well suited to deal with this scenario. This is due to the fact that they either act as black boxes or do not support incremental classification/learning. In this paper we introduce SS3, a novel supervised learning model for text classification that naturally supports these aspects. SS3 was designed to be used as a general framework to deal with ERD problems. We evaluated our model on the CLEF's eRisk2017 pilot task on early depression detection. Most of the 30 contributions submitted to this competition used state-of-the-art methods. Experimental results show that our classifier was able to outperform these models and standard classifiers, despite being less computationally expensive and having the ability to explain its rationale.

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“…Most investigations aim to detect events that have already occurred [68]. Escalante et al [68] proposed an approach to detect intent to commit fraud and aggression on social network posts. During the detection process, an analysis of documents which were categorized by ML algorithms is performed.…”

Section: Machine Learning and Mixed Learning Based Solutionsmentioning

confidence: 99%

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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Early detection of deception and aggressiveness using profile-based representations

Cited by 33 publications

References 24 publications

Early Text Classification Using Multi-Resolution Concept Representations

Early Text Classification Using Multi-Resolution Concept Representations

A text classification framework for simple and effective early depression detection over social media streams

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

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