Opinion modeling on social media and marketing aspects

We introduce and discuss kinetic models of opinion formation on social networks in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion formation model is subsequently coupled with a kinetic model describing the spreading of popularity of a product on the web through a social network. Numerical experiments on the underlying kinetic models show a good qualitative agreement with some measured trends of hashtags on social media websites and illustrate how companies can take advantage of the network structure to obtain at best the advertisement of their products

Opinion modeling on social media and marketing aspects

We introduce and discuss kinetic models of opinion formation on social networks in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion formation model is subsequently coupled with a kinetic model describing the spreading of popularity of a product on the web through a social network. Numerical experiments on the underlying kinetic models show a good qualitative agreement with some measured trends of hashtags on social media websites and illustrate how companies can take advantage of the network structure to obtain at best the advertisement of their products

The effects of travelling reasons on social media resources and tourist expectations

Esta investigación tiene como objetivo examinar la relación de las fuentes del contenido generado por el usuario (UGC) en las redes sociales, que proviene generalmente de fuentes de lazos fuertes y fuentes de lazos débiles, en la generación de expectativas turísticas sobre los recursos básicos y los recursos o factores de apoyo de los destinos. También se analiza el efecto moderador de las razones para viajar en la relación de las fuentes UGC y las expectativas turísticas. Para esta investigación, se recogieron 375 encuestas. Los resultados señalan que las razones o motivos del viaje son un factor importante a considerar en la generación de las expectativas turísticas, y en nuestro caso, el UGC que provenía de las fuentes de lazos débiles influyen de manera significativa en la generación de expectativas del turista cuando viaja por motivos de trabajo.This research aims to examine the relationship of user generated content (UGC) sources in social media which is provided by strong-tie sources and weak-tie sources on tourist expectations on core resources and factor supporting of the destinations, and also analyze the moderate effect of the reasons of travelling on the relationship of UGC sources and tourist expectations. 375 samples were collected. The results demonstrate that travelling reasons is an important factor to consider about the origin of tourist expectations. The UGC that was provided by weak-tie source has influence on tourist expectations when they travel with business reason

A Bayesian-Based Approach for Public Sentiment Modeling

Public sentiment is a direct public-centric indicator for the success of effective action planning. Despite its importance, systematic modeling of public sentiment remains untapped in previous studies. This research aims to develop a Bayesian-based approach for quantitative public sentiment modeling, which is capable of incorporating uncertainty and guiding the selection of public sentiment measures. This study comprises three steps: (1) quantifying prior sentiment information and new sentiment observations with Dirichlet distribution and multinomial distribution respectively; (2) deriving the posterior distribution of sentiment probabilities through incorporating the Dirichlet distribution and multinomial distribution via Bayesian inference; and (3) measuring public sentiment through aggregating sampled sets of sentiment probabilities with an application-based measure. A case study on Hurricane Harvey is provided to demonstrate the feasibility and applicability of the proposed approach. The developed approach also has the potential to be generalized to model various types of probability-based measures

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201