scholarly journals Similarity approximation of Twitter Profiles

2021 ◽  
Author(s):  
Niloufar Shoeibi ◽  
Nastaran Shoeibi ◽  
Pablo Chamoso ◽  
Zakie Alizadehsani ◽  
Juan M. Corchado
Keyword(s):  
Social Media ◽  
Language Processing ◽  
User Satisfaction ◽  
Intelligent System ◽  
Media Analysis ◽  
Related Information ◽  
Social Media Platforms ◽  
Dynamic Time ◽  
Content Similarity ◽  
Processing Techniques

Social media platforms are entirely an undeniable part of the lifestyle from the past decade. Analyzing the information being shared is a crucial step to understand humans behavior. Social media analysis is aiming to guarantee a better experience for the user and risen user satisfaction. But first, it is necessary to know how and from which aspects to compare users with each other. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official Twitter API. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for comparison of the behavioral ratios of two profiles. Next, Graph Network Analysis is used for monitoring the interactions of the user and its audience; for estimating the similarity of graphs, Jaccard similarity is used. Finally, for the Content similarity measurement, natural language processing techniques for preprocessing and TF-IDF for feature extraction are employed and then compared using the cosine similarity method. Results have presented the similarity level of different profiles. As the case study, people with the same interest show higher similarity. This way of comparison is helpful in many other areas. Also, it enables to find duplicate profiles; those are profiles with almost the same behavior and content.

scholarly journals Similarity Approximation of Twitter Profiles

2021 ◽  
Author(s):  
Niloufar Shoeibi ◽  
Nastaran Shoeibi ◽  
Pablo Chamoso ◽  
Zakie AlizadehSani ◽  
Juan M. Corchado
Keyword(s):  
Social Media ◽  
Feature Extraction ◽  
User Satisfaction ◽  
Intelligent System ◽  
Media Analysis ◽  
Classification Model ◽  
Feature Extraction Method ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Content Similarity

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. However, first, it is necessary to know how and from which aspects to compare users. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official TwitterAPI. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for the comparison of the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

scholarly journals An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde ◽  
Garima Sharma
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Gold Mine ◽  
Social Media Platforms ◽  
Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

scholarly journals MO’ CHARACTERS MO’ PROBLEMS: ONLINE SOCIAL MEDIA PLATFORM CONSTRAINTS AND MODES OF COMMUNICATION

2020 ◽  
Author(s):  
Lewis Mitchell ◽  
Joshua Dent ◽  
Joshua Ross
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
English Language ◽  
Length Change ◽  
Reading Level ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Media Platform ◽  
Processing Techniques

It is widely accepted that different online social media platforms produce different modes of communication, however the ways in which these modalities are shaped by the constraints of a particular platform remain difficult to quantify. On 7 November 2017 Twitter doubled the character limit for users to 280 characters, presenting a unique opportunity to study the response of this population to an exogenous change to the communication medium. Here we analyse a large dataset comprising 387 million English-language tweets (10% of all public tweets) collected over the September 2017--January 2018 period to quantify and explain large-scale changes in individual behaviour and communication patterns precipitated by the character-length change. Using statistical and natural language processing techniques we find that linguistic complexity increased after the change, with individuals writing at a significantly higher reading level. However, we find that some textual properties such as statistical language distribution remain invariant across the change, and are no different to writings in different online media. By fitting a generative mathematical model to the data we find a surprisingly slow response of the Twitter population to this exogenous change, with a substantial number of users taking a number of weeks to adjust to the new medium. In the talk we describe the model and Bayesian parameter estimation techniques used to make these inferences. Furthermore, we argue for mathematical models as an alternative exploratory methodology for "Big" social media datasets, empowering the researcher to make inferences about the human behavioural processes which underlie large-scale patterns and trends.

scholarly journals Music Recommendation System based on Emotions in User’s Social Media behaviour

2021 ◽  
Author(s):  
Ravidu Perera
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Recommendation System ◽  
Media Content ◽  
Music Recommendation ◽  
Social Media Platforms ◽  
Emotional Behaviour ◽  
Emotional Conflicts ◽  
Processing Techniques ◽  
Music Recommendation System

<p>The modern lifestyle makes people more competitive. It can lead to more stressful situations in our lifestyle. With the changes in human emotional behaviour, they tend to share their feelings on social media platforms rather than communicating with relatives. Studies proved that people used to listen to music to avoid emotional situations in their life. But there is no proper way to get the most accurate music to listen to and avoid emotional conflicts.</p> <p> </p> <p>Resolving these conflicts, the music recommendation system based on emotion introduced. It analyses the users' recent social media content and detects the various kind of emotions. To ensure that the suggested music is relevant to users emotions, the lyrics analysing was done using natural language processing techniques to identify the music emotions. Most people pay attention to the meaning of the songs, that was the major reason to consider the emotions of the lyrics.</p>

Music Recommendation System based on Emotions in User’s Social Media behaviour

2021 ◽  
Author(s):  
Ravidu Perera
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Recommendation System ◽  
Media Content ◽  
Music Recommendation ◽  
Social Media Platforms ◽  
Emotional Behaviour ◽  
Emotional Conflicts ◽  
Processing Techniques ◽  
Music Recommendation System

<p>The modern lifestyle makes people more competitive. It can lead to more stressful situations in our lifestyle. With the changes in human emotional behaviour, they tend to share their feelings on social media platforms rather than communicating with relatives. Studies proved that people used to listen to music to avoid emotional situations in their life. But there is no proper way to get the most accurate music to listen to and avoid emotional conflicts.</p> <p> </p> <p>Resolving these conflicts, the music recommendation system based on emotion introduced. It analyses the users' recent social media content and detects the various kind of emotions. To ensure that the suggested music is relevant to users emotions, the lyrics analysing was done using natural language processing techniques to identify the music emotions. Most people pay attention to the meaning of the songs, that was the major reason to consider the emotions of the lyrics.</p>

scholarly journals HPV Vaccination: Polish-Language Facebook Discourse Analysis

2022 ◽  
Vol 19 (2) ◽  
pp. 914
Author(s):  
Karolina Sobeczek ◽  
Mariusz Gujski ◽  
Filip Raciborski
Keyword(s):  
Social Media ◽  
Human Papillomavirus ◽  
Qualitative Analysis ◽  
Hpv Vaccination ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Related Information ◽  
Social Media Platforms ◽  
Polish Language ◽  
Negative Sentiment

Social media platforms are widely used for spreading vaccine-related information. The objectives of this paper are to characterize Polish-language human papillomavirus (HPV) vaccination discourse on Facebook and to trace the possible influence of the COVID-19 pandemic on changes in the HPV vaccination debate. A quantitative and qualitative analysis was carried out based on data collected with a tool for internet monitoring and social media analysis. We found that the discourse about HPV vaccination bearing negative sentiment is centralized. There are leaders whose posts generate the bulk of anti-vaccine traffic and who possess relatively greater capability to influence recipients’ opinions. At the beginning of the COVID-19 pandemic vaccination debate intensified, but there is no unequivocal evidence to suggest that interest in the HPV vaccination topic changed.

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