scholarly journals A Model for Estimating the Posting Frequency in an Online Social Media with Incomplete Data Using Objective Determinants of Users’ Behaviour

2021 ◽  
pp. 77-95
Author(s):  
Валерия Фуатовна Столярова ◽  
Александра Витальевна Торопова ◽  
Александр Львович Тулупьев
Keyword(s):  
Social Media ◽  
Information Criteria ◽  
User Profiling ◽  
Training Data ◽  
Practical Significance ◽  
Training Dataset ◽  
Individual Risk ◽  
Limited Data ◽  
Area Of Interest ◽  
Online Social Media

Профилирование пользователя онлайн социальной сети включает задачу оценки частоты (интенсивности) различных действий, в частности, публикации постов. Однако в силу ресурсных ограничений, может быть доступна только неполная информация о времени публикации нескольких последних постов, полученная, например, в рамках интервью. Оценка интенсивности постинга на основании таких данных востребована при анализе индивидуального риска, связанного с использованием онлайн социальных сетей. В статье предложена расширенная байесовская сеть доверия, которая использует не только информацию о времени публикации последних постов, но и объективные данные из профиля пользователя: пол, возраст, число друзей. Для обучения и демонстрации работы модели были собраны данные о публикации постов случайных пользователей в онлайн социальной сети ВКонтакте. Расширенная структура имеет более высокое значение информационного критерия Акаике по сравнению с упрощенной. User profiling is related to the problem of estimation of frequency of certain user’s actions in an online social media, like posting. But due to limited resources the only information available may be imprecise information on several last episodes of posting, that can be gathered via an interview. The frequency of posting estimates with such limited data may be used in the individual risk assessment that is connected with the use of online social media, for example, in medicine or cybersecurity. In the paper the Bayes belief network (BBN) for this problem is constructed, that incorporates not only the limited data on times of several last posts in an online social media, but the objective data about the user’s profile: age, sex, and friends count. With the training dataset gathered via API VKontakte we estimated conditional probability tables for two expert BBN structures (existing reduced structure based only on dates of several last posts and novel extended structure with objective behavior determinants incorporated) and automatically learned the optimal structure for the training data. Both extended models (expert and learned) showed lower values of the information criteria (Akaike information criteria and bayesian information criteria). Then with the test dataset the classification problem of the true frequency value was assessed. All three models showed similar results based on accuracy, kappa and average accuracy characteristics. This result is related to the weak strength of arcs between frequency variable and objective behavior determinants. But nevertheless the use of such variables is important in the application in order to construct the comprehensive structure of the knowledge in the area of interest. The practical significance of the work lies in the possibility of applying the proposed model to assess the posting frequency in the online social network, in particular in the tasks of modeling risk in the field of public health and socio-cybersecurity.

Predicting Consumers’ Decision-Making Styles by Analyzing Digital Footprints on Facebook

2019 ◽  
Vol 18 (02) ◽  
pp. 601-627 ◽  
Author(s):  
Yuh-Jen Chen ◽  
Yuh-Min Chen ◽  
Yu-Jen Hsu ◽  
Jyun-Han Wu
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Training Dataset ◽  
Consumer Decision Making ◽  
Consumer Decision ◽  
Digital Footprint ◽  
Online Social Media ◽  
Decision Making Styles ◽  
Prediction Mechanism ◽  
Digital Footprints

In the past, enterprises used time-consuming questionnaire surveys and statistical analysis to formulate consumer profiles. However, explosive growth in social media had produced enormous quantities of texts, images, and videos, which is sometimes referred to as a digital footprint. This provides an alternative channel for enterprises seeking to gain an objective understanding of their target consumers. Facilitating the analysis of data used in the formulation of a marketing strategy based on digital footprints from online social media is crucial for enterprises seeking to enhance their competitive advantage in today’s markets. This study develops an approach for predicting consumer decision-making styles by analyzing digital footprints on Facebook to assist enterprises in rapidly and correctly mastering the consumption profile of consumers, thereby reducing marketing costs and promoting customer satisfaction. This objective can be achieved by performing the following tasks: (i) designing a process for predicting consumer decision-making styles based on the analysis of digital footprints on Facebook, (ii) developing techniques related to consumer decision-making style prediction, and (iii) implementing and evaluating a consumer decision-making style prediction mechanism. In the practical experiment, we obtained questionnaires and various digital footprint contents (including “Likes,” “Status,” and “Photo/Video”) from 3304 participants in 2018, 2644 of which were randomly selected as a training dataset, with the remaining 660 participants forming a testing dataset. The experimental results indicated that the accuracy increased to 75.88% and proved that the approach proposed in this study can effectively predict consumers’ decision-making styles.

scholarly journals Social Bots Detection via Fusing BERT and Graph Convolutional Networks

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Qinglang Guo ◽  
Haiyong Xie ◽  
Yangyang Li ◽  
Wen Ma ◽  
Chao Zhang
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Robot ◽  
Training Data ◽  
User Privacy ◽  
Transductive Learning ◽  
Convolutional Network ◽  
Online Social Media ◽  
Single Text ◽  
Wide Range

The online social media ecosystem is becoming more and more confused because of more and more fake information and the social media of malicious users’ fake content; at the same time, unspeakable pain has been brought to mankind. Social robot detection uses supervised classification based on artificial feature extraction. However, user privacy is also involved in using these methods, and the hidden feature information is also ignored, such as semi-supervised algorithms with low utilization rates and graph features. In this work, we symmetrically combine BERT and GCN (Graph Convolutional Network, GCN) and propose a novel model that combines large scale pretraining and transductive learning for social robot detection, BGSRD. BGSRD constructs a heterogeneous graph over the dataset and represents Twitter as nodes using BERT representations. Corpus learning via text graph convolution network is a single text graph, which is mainly built for corpus-based on word co-occurrence and document word relationship. BERT and GCN modules can be jointly trained in BGSRD to achieve the best of merit, training data and unlabeled test data can spread label influence through graph convolution and can be carried out in the large-scale pre-training of massive raw data and the transduction learning of joint learning representation. The experiment shows that a better performance can also be achieved by BGSRD on a wide range of social robot detection datasets.

Impact of Online Social Media on Consumer Decision Making: A Study in Dehradun

2012 ◽  
Vol 3 (5) ◽  
pp. 379-381
Author(s):  
Dr. Aruna Kumar Mishra ◽  
◽  
Narendra Kumar Narendra Kumar ◽  
Abhishek Sharma
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Consumer Decision Making ◽  
Consumer Decision ◽  
Online Social Media

DERADICALISATION IDEAS IN SOCIAL MEDIA THROUGH THE RELIGIOUS-MULTICULTURAL THERAPY APPROACH TO THE DA'WAH CULTURE

2020 ◽  
Vol 24 (1) ◽  
pp. 58
Author(s):  
Anwar Hafidzi
Keyword(s):  
Social Media ◽  
Real World ◽  
Environmental Conditions ◽  
Islamic Law ◽  
Research Tool ◽  
Therapy Approach ◽  
The Real ◽  
Multicultural Therapy ◽  
Online Social Media ◽  
Library Research

This research begins with an understanding of the endemic radicalism of society, not only of the real world, but also of various online social media. This study showed that the avoidance of online radicalism can be stopped as soon as possible by accusing those influenced by the radical radicality of a secular religious approach. The methods used must be assisted in order to achieve balanced understanding (wasathiyah) under the different environmental conditions of the culture through recognizing the meaning of religion. The research tool used is primarily library work and the journal writings by Abu Rokhmad, a terrorist and radicalise specialist. The results of this study are that an approach that supports inclusive ism will avoid the awareness of radicalization through a heart-to-heart approach. This study also shows that radical actors will never cease to argue dramatically until they are able to grasp different views from Islamic law, culture, and families.Keywords: radicalism, deradicalization, multiculturalism, culture, religion, moderate.Penelitian ini berawal dari paham radikalisme yang telah mewabah di masyarakat, bukan hanya di dunia nyata, bahkan sudah menyusup di berbagai media sosial online. Penelitian ini menemukan bahwa cara menangkal radikalisme online dapat dilakukan pencegahan sedini mungkin melalui pendekatan konseling religius multikultural terhadap mereka yang terkena paham radikal radikal. Diantara teknik yang digunakan adalah melalui pemahaman tentang konsep agama juga perlu digalakkan agar memunculkan pemahaman yang moderat (wasathiyah) diberbagai keadaan lingkungan masyarakat. Metode yang digunakan untuk penelitian ini adalah library research dengan sumber utama adalah karya dan jurnal karya Abu Rokhmad seorang pakar dalam masalah terorisme dan radikalisme. Temuan penelitian ini adalah paham radikalisasi itu dapat dihentikan dengan pendekatan hati ke hati dengan mengedepankan budaya yang multikultural. Kajian ini juga membuktikan bahwa pelaku paham radikal tidak akan pernah berhenti memberikan argumen radikal kecuali mampu memahami perbedaan pendapat yang bersumber dari syariat Islam, lingkungan sosial, dan keluarga.Kata kunci: radikalisme, deradikalisasi, multikultural, budaya, agama, moderat.

Customer Revenue Sharing Program in Online Social Media

2012 ◽  
Author(s):  
Fouad H. Mirzaei ◽  
Fredrik Odegaard ◽  
Xinghao Yan
Keyword(s):  
Social Media ◽  
Revenue Sharing ◽  
Online Social Media

DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Research Work ◽  
Training Data ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cytokine Signaling ◽  
Minority Class ◽  
Independent Dataset ◽  
Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

Knowledge, awareness and perceptions of diabetes mellitus among the Saudi population

2020 ◽  
Vol 9 (6) ◽  
pp. 413-422
Author(s):  
Muhammad H Mujammami ◽  
Abdulaziz A Alodhayani ◽  
Mohammad Ibrahim AlJabri ◽  
Ahmad Alhumaidi Alanazi ◽  
Sultan Sayyaf Alanazi ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Social Media ◽  
Risk Factor ◽  
Healthcare Workers ◽  
Health Workers ◽  
Saudi Population ◽  
Limited Data ◽  
High Prevalence ◽  
Associated Risk Factors

Background: High prevalence of undiagnosed cases of diabetes mellitus (DM) has increased over the last two decades, most patients with DM only become aware of their condition once they develop a complication. Limited data are available regarding the knowledge and awareness about DM and the associated risk factors, complications and management in Saudi society. Aim: This study aimed to assess knowledge of DM in general Saudi society and among Saudi healthcare workers. Results: Only 37.3% of the participants were aware of the current DM prevalence. Obesity was the most frequently identified risk factor for DM. Most comparisons indicated better awareness among health workers. Conclusion: A significant lack of knowledge about DM in Saudi society was identified. Social media and educational curriculum can improve knowledge and awareness of DM.

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