Constructing and analyzing spatial-social networks from location-based social media data

Abstract Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined user’s sentiments on these platforms to study their behaviour in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work [1], by proposing an unsupervised approach to automatically detect extreme opinions/posts in social networks. We have evaluated our performance on five different social network and media datasets. In this work, we use the semi-supervised approach BERT to check the accuracy of our classified dataset. The latter task shows that, in these datasets, posts that were previously classified as negative or positive are, in fact, extremely negative or positive in many cases.

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Are Social Media Data Pushing Overtourism? The Case of Barcelona and Chinese Tourists

Sustainability ◽

10.3390/su11123356 ◽

2019 ◽

Vol 11 (12) ◽

pp. 3356 ◽

Cited By ~ 20

Author(s):

Alonso-Almeida ◽

Borrajo-Millán ◽

Yi

Keyword(s):

Social Networks ◽

Social Media ◽

Local Population ◽

Travel Behaviour ◽

Social Media Data ◽

Large Numbers ◽

Tourism Sector ◽

Chinese Tourists ◽

Media Data

Overtourism spoils the good economic and social results produced by the tourism sector, causing reductions in the quality of service of the tourist destination and rejection by the local population. Previous literature has suggested that social networks and new electronic channels could be accelerators of the process of overcrowding destinations; however, this link has not been established. For this reason, in this exploratory study, the influence of social networks on overtourism is analysed using Barcelona as a base, as Barcelona is a massively popular destination in the country that is second in the world in reception of tourists to Spain. This study is also focused on Chinese tourism, which brings large numbers of tourists and presents great economic potential. Two types of study have been used: big data techniques applied to social media with sentimental analysis, and analysis of travel packages offered in China to travel to Spain. Relevant results are obtained to understand the influence of social networks on the travel behaviour of tourists, possible contributions to overtourism, and recommendations for the management of tourism.

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PRINCIPLES OF SEARCHING AND SORTING OPTIMIZATION IN SOCIAL NETWORKS USING A MULTI-FACTOR ASSESSMENT SYSTEM

Bionics of Intelligence ◽

10.30837/bi.2019.1(92).08 ◽

2019 ◽

Vol 1 (92) ◽

pp. 47-51

Author(s):

Maksim V. Shopynskyi ◽

N. V. Golian ◽

I. V. Afanasieva

Keyword(s):

Social Networks ◽

Social Media ◽

Assessment System ◽

Combined System ◽

Social Media Data ◽

Advantages And Disadvantages ◽

Active Research ◽

Social Entities ◽

Media Data

The analysis of social networks, which focuses on the relationship between social entities today is an area of active research. It is a set of tools for research, in particular, in combination with artificial intelligence methods such as machine learning, deep learning. The paper examined the current quality of the assessment of information in social networks, analyzed the methods of searching and sorting information in various social networks, as well as the process of providing recommendations to users. Social media data is an inexhaustible source of research and business opportunities. In general, social media data is information gathered from social networks that shows how users interact with content. Methods of improving search results for personalizing recommendations in social networks are given. These indicators and statistics provide an effective understanding of the strategy of behavior in social networks. The advantages and disadvantages of a multifactor assessment system are considered. The possible ways of integrating the combined system of evaluating information elements by the user to optimize search queries and filtering big data are identified.

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An Overview on Protecting User Private-Attribute Information on Social Networks

Handbook of Research on Cyber Crime and Information Privacy - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-5728-0.ch006 ◽

2021 ◽

pp. 102-117

Author(s):

Walaa Alnasser ◽

Ghazaleh Beigi ◽

Huan Liu

Keyword(s):

Social Networks ◽

Social Media ◽

Online Social Networks ◽

Differential Privacy ◽

Future Research ◽

Online Publishing ◽

Social Media Data ◽

User Data ◽

Inference Attacks ◽

Media Data

Online social networks enable users to participate in different activities, such as connecting with each other and sharing different contents online. These activities lead to the generation of vast amounts of user data online. Publishing user-generated data causes the problem of user privacy as this data includes information about users' private and sensitive attributes. This privacy issue mandates social media data publishers to protect users' privacy by anonymizing user-generated social media data. Existing private-attribute inference attacks can be classified into two classes: friend-based private-attribute attacks and behavior-based private-attribute attacks. Consequently, various privacy protection models are proposed to protect users against private-attribute inference attacks such as k-anonymity and differential privacy. This chapter will overview and compare recent state-of-the-art researches in terms of private-attribute inference attacks and corresponding anonymization techniques. In addition, open problems and future research directions will be discussed.

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