Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly

García-Méndez, Silvia; Leal, Fátima; Malheiro, Benedita; Burguillo-Rial, Juan C.; Veloso, Bruno; Chis, Adriana E.; González–Vélez, Horacio

doi:10.1016/j.simpat.2022.102616

Cited by 7 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Very often, online users are communicating with AI technologies without even knowing. Recent research indicated that more chatbots (as opposed to human customer services representatives) are on the other end of the line, replying to the consumers' questions [28][29][30].…”

Section: 1mentioning

confidence: 99%

Chatbot Recommender Systems in Tourism: A Systematic Review and a Benefit-Cost Analysis

Camilleri¹,

Troise²

2023

SSRN Journal

View full text Add to dashboard Cite

Section: 1mentioning

confidence: 99%

Chatbot Recommender Systems in Tourism: A Systematic Review and a Benefit-Cost Analysis

Camilleri¹,

Troise²

2023

SSRN Journal

View full text Add to dashboard Cite

“…Besides, most surveyed works implemented offline processing. The only exception is the stream-based quality and popularity profiling proposed by [18], which enables model updating in real-time, along with the user profiling work of [19] that identifies benign and malign human and nonhuman (bots) contributors. Since the classification problem in the latter work is different, the content of the review is not considered.…”

Section: A Profilingmentioning

confidence: 99%

“…Feature selection is performed through a meta-transformer wrapper method. Mainly, a meta-transformer method can be used with any estimator for feature selection, while a wrapper method allows the exploitation of an underlying ML model for the feature importance computation [19]. It wraps the classification algorithm -Random Forest (RF) classifier -and selects features based on importance weights.…”

Section: B: Feature Engineeringmentioning

confidence: 99%

“…The following binary classification algorithms were selected from scikit-learn 18 and scikit-multiflow 19 and applied without hyper-parameter optimization.…”

Section: ) Incremental Classificationmentioning

confidence: 99%

“…Available at https://scikit-learn.org/stable, December 2023 19. Available at https://scikit-multiflow.readthedocs.io/en/stable/api/api.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Interpretable Classification of Wiki-Review Streams

García-Méndez,

Leal,

Malheiro

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90 % values for all evaluation metrics (accuracy, precision, recall, and F-measure).INDEX TERMS Data reliability and fairness, data-stream processing and classification, synthetic data, transparency, vandalism, wikis.

show abstract