USING SOCIAL MEDIA DATA FOR PROBABLE CALCULATIONS USING SCENARIOUS MODELING METHODS: RESULTS OF ELECTORAL CAMPAIGNS

2021 ◽  
Vol 1 (2) ◽  
pp. 29-37
Author(s):  
A. A. Azarov ◽  
◽  
A. V. Suvorova ◽  
E. V. Brodovskaya ◽  
O. V. Vasileva ◽  
...  
Keyword(s):  
Social Networks ◽  
Political Parties ◽  
Random Variable ◽  
Social Media Data ◽  
Scenario Modeling ◽  
Electoral Campaigns ◽  
Modeling Methods ◽  
Electoral Support ◽  
Political Communities ◽  
Media Data

The article presents the application of scenario modeling methods to assess the potential for scaling electoral support for political parties through digital communications (communities in social networks) based on data obtained from social networks. An analysis of communities in several social networks was carried out, various indicators were downloaded, reflecting the activity of both communities and users of such communities. Based on these data, various aggregates were calculated. Then a software package was developed that implements scenario modeling based on various identified indicators. The scenarios provide for the development of groups in social networks, depending on the activity of these groups. In this case, the activity is given by a random variable with a normal distribution. To test the developed algorithms, indicators of political communities in social networks were used.

scholarly journals Detect Extreme Sentiments on Social Networks using BERT

2021 ◽  
Author(s):  
Muhammad Luqman Jamil ◽  
Sebastião Pais ◽  
João Cordeiro ◽  
Gaël Dias
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Public Opinion ◽  
Social Network ◽  
Social Networking ◽  
Online Social Networking ◽  
Social Media Data ◽  
Preceding Work ◽  
Unsupervised Approach ◽  
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.

Using Social Media Data to Analyse Issue Engagement During the 2017 German Federal Election

2022 ◽  
Vol 22 (1) ◽  
pp. 1-25
Author(s):  
Florian Meier ◽  
Alexander Bazo ◽  
David Elsweiler
Keyword(s):  
Social Media ◽  
Political Parties ◽  
Political Science ◽  
Computational Social Science ◽  
The Political ◽  
Political Strategy ◽  
Political Debate ◽  
Social Media Data ◽  
Political Left ◽  
Media Data

A fundamental tenet of democracy is that political parties present policy alternatives, such that the public can participate in the decision-making process. Parties, however, strategically control public discussion by emphasising topics that they believe will highlight their strengths in voters’ minds. Political strategy has been studied for decades, mostly by manually annotating and analysing party statements, press coverage, or TV ads. Here we build on recent work in the areas of computational social science and eDemocracy, which studied these concepts computationally with social media. We operationalize issue engagement and related political science theories to measure and quantify politicians’ communication behavior using more than 366k Tweets posted by over 1,000 prominent German politicians in the 2017 election year. To this end, we first identify issues in posted Tweets by utilising a hashtag-based approach well known in the literature. This method allows several prominent issues featuring in the political debate on Twitter that year to be identified. We show that different political parties engage to a larger or lesser extent with these issues. The findings reveal differing social media strategies by parties located at different sides of the political left-right scale, in terms of which issues they engage with, how confrontational they are and how their strategies evolve in the lead-up to the election. Whereas previous work has analysed the general public’s use of Twitter or politicians’ communication in terms of cross-party polarisation, this is the first study of political science theories, relating to issue engagement, using politicians’ social media data.

scholarly journals The climate change movement and political parties: Mechanisms of social media and interaction during the 2019 electoral period in Finland

2021 ◽  
Vol 3 (1) ◽  
pp. 40-60
Author(s):  
Sonja Savolainen ◽  
Tuomas Ylä-Anttila
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Social Movements ◽  
Political Parties ◽  
Comparative Studies ◽  
Social Media Data ◽  
Interaction Dynamics ◽  
The Government ◽  
Media Data

Abstract Building on the framework of electoral contention, we investigate the interaction dynamics between social movements and political parties during elections. We argue that social media today is an important venue for these interactions, and consequently, analysing social media data is useful for understanding the shifts in the conflict and alliance structures between movements and parties. We find that Twitter discussions on the climate change movement during the 2019 electoral period in Finland reveal a process of pre-election approaching and post-election distancing between the movement and parties. The Greens and the Left formed mutually beneficial coalitions with the movement preceding the elections and took distance from one another after these parties entered the government. These findings suggest that research on movement-party interaction should pay more attention to social media and undertake comparative studies to assess whether the approaching-distancing process and its constituent mechanisms characterise movements beyond the climate strikes in Finland.

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

2019 ◽  
Vol 11 (12) ◽  
pp. 3356 ◽  
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.

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

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.

scholarly journals Fake News Case Study during the Australian 2019 General Election

2020 ◽  
Vol 24 ◽  
Author(s):  
Matthew Warren
Keyword(s):  
Social Media ◽  
Political Parties ◽  
Ethical Issues ◽  
General Election ◽  
Fake News ◽  
Social Media Data ◽  
Research Perspective ◽  
Research Findings ◽  
Media Data

Social media is used by all aspects of society from citizens to businesses, but it also now used by political parties. Political parties use social media to engage with voters as a method of attract new voters or reinforcing the views of political parties’ current supporters. An important consideration is the ethical conduct of political parties and politicians in how they use social media. It is now recognized that social media can also have negative aspects seen by the introduction of Fake News. These negative aspects of social media are often overlooked and have not been explored from a research perspective. This paper looks at the Australian 2019 General Election and discusses a major Fake News example that occurred during that election. The paper will also describe the different types of social media data was collected during the study and also present the analysis of the data collected as well discussing the research findings including the ethical issues.

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

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.

Sign in / Sign up

Export Citation Format

Share Document