The Use of Online Social Networks: Comparing Moroccan and French Communities on Facebook

Idrais, Jaafar; Moudene, Yassine El; Abassi, Rida El; Sabour, Abderrahim

doi:10.1007/978-3-031-26254-8_129

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In 2002, Mark Zuckerberg set up a network that connects students, first called Facemash, then Facebook ※ (FB) in 2004 [20][21][22] . Five years after, FB launched the "Like" button, an e-emotion represented by the thumb in the air, which remains the most popular reaction by a wide margin [23] .…”

Section: Introductionmentioning

confidence: 99%

Gender-Based Analysis of User Reactions to Facebook Posts

Moudene,

Idrais,

El Abassi

et al. 2024

Big Data Min. Anal.

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Online Social Networks (OSNs) are based on the sharing of different types of information and on various interactions (comments, reactions, and sharing). One of these important actions is the emotional reaction to the content. The diversity of reaction types available on Facebook (namely FB) enables users to express their feelings, and its traceability creates and enriches the users' emotional identity in the virtual world.This paper is based on the analysis of 119 875 012 FB reactions (Like, Love, Haha, Wow, Sad, Angry, Thankful, and Pride) made at multiple levels (publications, comments, and sub-comments) to study and classify the users' emotional behavior, visualize the distribution of different types of reactions, and analyze the gender impact on emotion generation. All of these can be achieved by addressing these research questions: who reacts the most? Which emotion is the most expressed?

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Section: Introductionmentioning

confidence: 99%

Gender-Based Analysis of User Reactions to Facebook Posts

Moudene,

Idrais,

El Abassi

et al. 2024

Big Data Min. Anal.

Self Cite

View full text Add to dashboard Cite

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“…In the 1970s, Box and Jenkins introduced another perspective on time series modelling [5], named Box and Jenkins methodology, it is based on the Wold's representation theorem [6]- [9]; in fact, once a process is (weakly) stationary, it can be written as the weighted sum of past shocks. This is how the notion of stationary becomes fundamental to the analysis process [10]. However, a seasonal and trend time series is considered to be non-stationary and often needs to be made stationary, using a certain seasonal adjustment method [11], before most modelling and forecasting processes take place.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of recurrent neural networks for seasonal trended time series modelling

Abassi,

Idrais,

Sabour

2023

IJECE

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Seasonal time series with trends are the most common data sets used in forecasting. This work focuses on the automatic processing of a non-pre-processed time series by studying the efficiency of recurrent neural networks (RNN), in particular both long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM) extensions, for modelling seasonal time series with trend. For this purpose, we are interested in the learning stability of the established systems using the mean average percentage error (MAPE) as a measure. Both simulated and real data were examined, and we have found a positive correlation between the signal period and the system input vector length for stable and relatively efficient learning. We also examined the white noise impact on the learning performance.

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The Use of Online Social Networks: Comparing Moroccan and French Communities on Facebook

Cited by 2 publications

References 12 publications

Gender-Based Analysis of User Reactions to Facebook Posts

Gender-Based Analysis of User Reactions to Facebook Posts

Efficiency of recurrent neural networks for seasonal trended time series modelling

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