Value-Based Retweet Prediction on Twitter

Kakar, Surbhi; Dhaka, Deepali; Mehrotra, Monica

doi:10.31449/inf.v45i2.3465

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Profiling is the process of modeling stakeholders according to their contributions and interactions (Kakar et al, 2021;García-Méndez et al, 2022b). In the case of spam detection, individual profiles are built from the content generated by each stakeholder, humans or bots alike.…”

Section: Profiling and Classificationmentioning

confidence: 99%

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

de Arriba-Pérez,

García-Méndez,

Leal

et al. 2024

Informatica

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Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87 % spam F-measure.

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Section: Profiling and Classificationmentioning

confidence: 99%

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

de Arriba-Pérez,

García-Méndez,

Leal

et al. 2024

Informatica

View full text Add to dashboard Cite

show abstract

“…In a study of one hundred Twitter users analyzing online behavior, with an accuracy of 75.13 percent, the authors were able to predict their preferences and deeper suites of personalities like openness and agreeableness (Mahajan et al, 2022). Value systems or personal beliefs could be an essential predictor of why someone engages in retweets and are sometimes as effective as traditional machine learning models like Random Forest, XGB, and Logistic Regression (Kakar et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Applying Socmint to Extract Cyber Threat Intelligence From the Russia-Ukraine Conflict

2022

IJWI

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The paper applied SOCMINT (Social Media Intelligence) techniques to discover cybersecurity-related information from the contemporary Russia-Ukraine conflict. Using open-source tools and APIs, datasets created were assessed through topic modeling, thematic analysis (word cloud), Logit function, and neural network classification. The topic modeling and word cloud yielded trifling insights, but Logit and neural network classifier, MLP, suggested statistically significant features that were important to the outcome of the tweets with reasonable accuracy of 91%. Through the use of synthetic data (GaussianCopula) and feature selection(stepAIC), the model was extended to improve accuracy, which resulted in 96% accuracy, though, such competent performance requires further investigation. While deciphering the right intelligence is a challenge due to the unruly nature of social media, this nascent technique can be helpful with the proper framework and approach.

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“…As a consequence of the proliferation of social networking sites such as Facebook, LinkedIn, and Twitter [15], people's communication habits have been significantly altered [1]. As a consequence of this transition, predicting concerns that may develop among a large number of people has become more challenging.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Quality of Predicting the ill effects of Intensive Human Exposure to Social Networks using Ensemble Method

Gadiparthi

Reddy²

2022

IJCAI

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People may quickly obtain information through a variety of channels including social media, blogs, websites, and other online resources. These platforms have made it possible for information to be shared more easily. As a result, the amount of time that individuals spend on various social networking applications has increased. This research predicts the effects of human exposure to social networks in the near future. In this work a competent model for predicting the ill-effects is provided, that is both accurate and efficient. This model represents a combination of the independent models that have been operating independently so far. Thus each of these models makes a forecast and ultimate selection is decided based on whether or not there exists a majority of convergence of results from the operation of various models. By employing the majority voting system, this strategy attempts to take the benefits of the predictions produced by all the models while also to reduce the inaccuracies generated by each model. Theoretically, this model should outperform the use of individual models in terms of performance. Important features are extracted from the datasets using the proposed model, and the extracted features are then classified using an ensemble model that consists of four popular machine learning models: support vector machines (SVMs), logistic regression (logistic regression), random forest (random forest classification), and neural networks (NN). We have analyzed our prediction performance with the existing methods for number of times by changing the train set and test set data. In all the cases our novel method has been predicting with 3% to 4% improved performance in accuracy, precision, F-1 score, and specificity. From the dataset it has been achievable to attain the highest training and testing accuracy from among the existing models.Povzetek: Optimizacija kakovosti napovedovanja slabih učinkov intenzivne izpostavljenosti človeka družbenim omrežjem z uporabo ansambelske metode.

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Value-Based Retweet Prediction on Twitter

Cited by 8 publications

References 28 publications

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

Applying Socmint to Extract Cyber Threat Intelligence From the Russia-Ukraine Conflict

Optimizing the Quality of Predicting the ill effects of Intensive Human Exposure to Social Networks using Ensemble Method

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