COOPNet: Multi-Modal Cooperative Gender Prediction in Social Media User Profiling

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

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Identifying User Profile by Incorporating Self-Attention Mechanism based on CSDN Data Set

Data Intelligence ◽

10.1162/dint_a_00009 ◽

2019 ◽

Vol 1 (2) ◽

pp. 160-175 ◽

Cited By ~ 1

Author(s):

Junru Lu ◽

Le Chen ◽

Kongming Meng ◽

Fengyi Wang ◽

Jun Xiang ◽

...

Keyword(s):

Neural Network ◽

Social Media ◽

Neural Network Model ◽

User Profile ◽

User Profiling ◽

Technology Evaluation ◽

Data Set ◽

Value Prediction ◽

User Interests ◽

Evaluation Scores

With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.

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CIRCLE 2020

ACM SIGIR Forum ◽

10.1145/3483382.3483394 ◽

2020 ◽

Vol 54 (2) ◽

pp. 1-9

Author(s):

Iván Cantador ◽

Max Chevalier ◽

Massimo Melucci ◽

Josiane Mothe

Keyword(s):

Social Media ◽

Information Retrieval ◽

Recommender Systems ◽

Research Work ◽

User Profiling ◽

Joint Conference ◽

Information Retrieval Evaluation ◽

Wide Range

The Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020) is the first joint conference of the French, Italian, Spanish, and Swiss information retrieval communities. Although these communities had conceived the CIRCLE conference as a meeting and networking venue, because of the COVID-19 pandemic, they had to make the conference as fully virtual event. Nonetheless, the three days of conference gathered interesting studies and research work on a wide range of topics on information retrieval, such as topic and document modelling, query and ranking refinement, information retrieval in e-government, social media, recommender systems, information retrieval evaluation, indexing and annotation, user profiling and interaction, frameworks and systems, and semantic extraction.

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Research on Gender Prediction for Social Media User Profiling by machine learning method

2021 International Conference on Communications, Information System and Computer Engineering (CISCE) ◽

10.1109/cisce52179.2021.9445922 ◽

2021 ◽

Author(s):

Chuanbo Liu ◽

Fei Li ◽

Lin Li

Keyword(s):

Machine Learning ◽

Social Media ◽

User Profiling ◽

Machine Learning Method ◽

Learning Method

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

Fuzzy Systems and Soft Computing ◽

10.26456/fssc81 ◽

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.

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