Digital transformation, online advertising, and consumer behaviour

Gupta, Sahil; Paul, Justin; Stoner, Jennifer L.; Aggarwal, Arun

doi:10.1080/02650487.2024.2317632

International Journal of Advertising

2024

DOI: 10.1080/02650487.2024.2317632

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Digital transformation, online advertising, and consumer behaviour

Sahil Gupta,

Justin Paul,

Jennifer L. Stoner

et al.

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2024

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Cited by 4 publications

References 93 publications

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Understanding the optimal strategy of carbon labelled product advertising delivery: A dynamic differential equation analysis

Peng,

Tao,

Hong

et al. 2024

Journal of Retailing and Consumer Services

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Understanding the optimal strategy of carbon labelled product advertising delivery: A dynamic differential equation analysis

Peng,

Tao,

Hong

et al. 2024

Journal of Retailing and Consumer Services

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The relationship between digital marketing and product purchase behaviour in Turkey: A structural equations modelling approach

Armutcu,

Zeqiri,

Ibahrine

et al. 2024

Journal of Marketing Communications

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Natural Language Processing for Sentiment Analysis in Social Media Posts to Identify Suspicious Behaviour

Gagandeep,

Verma

2024

Abhigyan

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This article reports a study on the use of the natural language processing approach in the sentiment analysis of social media posts for detecting suspicious activities involving deep learning methods. The primary objective is to design an automated system capable of identifying unfavourable sentiments and patterns that imply suspicious activity such as cyberbullying, hate speech and disinformation. The methodology involves collecting a diverse data set of 2,000 posts from platforms such as X and Facebook, pre-processed by tokenisation and normalisation. The sentiment analysis model uses a long short-term memory (LSTM) network because it finds that the LSTM network is good for recognising long-term dependencies between texts. Other heuristics are used to give prominence to posts with malicious behavioural profiles to flag them accordingly. The model is designed and tuned to pre-labelled data sets. This work shows considerable improvement over previous techniques at categorising the posts as suspicious by showing higher accuracy, precision, recall and F1 score ratios. In doing so, this research positively impacts the enhancement of paranoid security or the totality of social media safety by offering a strong early detection mechanism for reducing risky behaviours on social networks.

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Digital transformation, online advertising, and consumer behaviour

Cited by 4 publications

References 93 publications

Understanding the optimal strategy of carbon labelled product advertising delivery: A dynamic differential equation analysis

Understanding the optimal strategy of carbon labelled product advertising delivery: A dynamic differential equation analysis

The relationship between digital marketing and product purchase behaviour in Turkey: A structural equations modelling approach

Natural Language Processing for Sentiment Analysis in Social Media Posts to Identify Suspicious Behaviour

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