Twitter

Twitter and Jihad: The Communication Strategy of ISIS edited by Monica Maggioni & Paolo Magri. Milan, Italy: ISPI, 2015. 168pp., $10 (p/b), ISBN 978-88-98014-66-8

Given the immense shifts the social networking sites and applications have brought about, a considerable number of researchers in the field of communication studies have turned to study different aspects of social media usage and factors influencing it. This study gathered data from 33318 US non-institutionalized citizens over 18 including 17079 females and 16239 males; they were members of web panelists of Pew, and their answers revealed that a majority of this online participants used a kind of social media. The results of this study revealed women use social media more than men, and religious people more than non-religious people. In addition, the results indicated that married people are the least users of social media in comparison with other marital groups. Our results showed that all demographics are significantly related to social media usage. But this significance can be somehow misleading because of weak practical effect sizes. Except for marital status and age Cramer’s V values are too small and their significance may have nothing to say but sensitivity to the degree of freedom.

Sentiment Analysis or opinion mining is NLP's method to computationally identify and categorize user opinions expressed in textual data.  Mainly it is used to determine the user's opinions, emotions, appraisals, or judgments towards a specific event, topic, product, etc. is positive, negative, or neutral. In this approach, a huge amount of digital data generated online from blogs and social media websites is gathered and analyzed to discover the insights and help make business decisions. Social media is web-based applications that are designed and developed to allow people to share digital content in real-time quickly and efficiently.  Many people define social media as apps on their Smartphone or tablet, but the truth is, this communication tool started with computers. It became an essential and inseparable part of human life. Most business uses social media to market products, promote brands, and connect to current customers and foster new business. Online social media data is pervasive. It allows people to post their opinions and sentiments about products, events, and other people in the form of short text messages. For example, Twitter is an online social networking service where users post and interact with short messages, called "tweets." Hence, currently, social media has become a prospective source for businesses to discover people's sentiments and opinions about a particular event or product. This paper focuses on the development of a Multinomial Naïve Bayes Based social media data emotion analyzer and sentiment classifier. This paper also explains various enriched methods used in pre-processing techniques. This paper also focuses on various Machine Learning Techniques and steps to use the text classifier and different types of language models.

Agovernmentshouldusesocialmediaforcommunicatingwithitscitizen.Theengagement index score is one of the methods for assessing the rate of governmental success in using social media as a tool in establishing interactive relationships with its citizen. In general, the engagement index score is obtained by calculating the number of posts, number of likes and comments, and so forth on a single social media account. Therefore, we propose an integrated engagement index score for three social media: Facebook, Youtube and Twitter. In this work, we carry out a study for local governments in Indonesia. The engagement index score was adopted from the previous research. However, we modified the formula to get a better distribution score.Our modified formula generates the same ranking sequences with previous research.Also, Facebook and Youtubes’ reaction are considered in this work to analyzes the quality of sentiments to a Facebook fan page and Youtube channel of localgovernments.

Brand community interactions and online customer support have become major platforms of brand sentiment strengthening and loyalty creation. Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of intense competition. In this research, there has been an attempt to collect near three million tweets of inbound customer requests and outbound brand responses of international enterprises for the purpose of brand sentiment analysis. The steps of CRISP-DM have been chosen as the reference guide for business and data understanding, data preparation, text mining, validation of results as well as the final discussion and contribution. A rich phase of text pre-processing has been conducted and various algorithms of sentiment analysis were applied for the purpose of achieving the most significant analytical conclusions over the sentiment trends. The findings have shown that the sentiment of customers toward a brand is significantly correlated with the proper response of brands to the brand community over social media as well as providing the customers with a deep feeling of reciprocal understanding of their needs in a mid-to-long range planning.

Twitter has provided a convenient platform to express feelings and opinions in different areas. Opinion mining in Twitter can be considered as studying the overall sentiment of a tweet. There are two general categories of sentiment analysis methods in the Persian language, linked-base methods and, content-based methods. In this study, we implement a new link-based method for improving opinion classification in the Persian language. To compare with the content-based method, we implement a content-based method using Naïve Bayes Method with two different weighting Methods: TF/IDF and Chi-Square. The TF/IDF method has good results in previous Persian language studies. The Chi-Square method has not been used in the Persian language researches, but the accuracy is fairly good in English. The results show that the improvement in the language-independent methods is remarkable and is in accordance with this research, the precision of the proposed algorithm for positive and negative comments was 98.87% and 97.87%, and the recall value for positive and negative comments was 99.24% and 96.84% respectively. The results also show that because of complexities in Persian syntax and lack of proper natural language processing tools in Persian, content-based algorithms operate poorly compared to English.

Sentiment analysis is the process of identifying and categorizing people’s emotions or opinions regarding various topics. The analysis of Twitter sentiment has become an increasingly popular topic in recent years. In this paper, we present several machine learning and a deep learning model to analysis sentiment of Persian political tweets. Our analysis was conducted using Bag of Words and ParsBERT for word representation. We applied Gaussian Naive Bayes, Gradient Boosting, Logistic Regression, Decision Trees, Random Forests, as well as a combination of CNN and LSTM to classify the polarities of tweets. The results of this study indicate that deep learning with ParsBERT embedding performs better than machine learning. The CNN-LSTM model had the highest classification accuracy with 89 percent on the first dataset and 71 percent on the second dataset. Due to the complexity of Persian, it was a difficult task to achieve this level of efficiency. The main objective of our research was to reduce the training time while maintaining the model's performance. As a result, several adjustments were made to the model architecture and parameters. In addition to achieving the objective, the performance was slightly improved as well.

Heart disease has emerged as the foremost global cause of mortality. Enhancing awareness and understanding of this disease, along with preventive strategies, is pivotal in averting its onset and diminishing mortality rates. Today, social networks have evolved into paramount information dissemination platforms owing to their user-friendly nature and widespread adoption. Among these, Twitter stands out as a prominent source of rich data that can be leveraged for educational and awareness purposes. While existing studies have evaluated the impact of social media on increasing health-related knowledge, there is a gap in research regarding the role of Twitter in increasing cardiovascular disease awareness and prevention from different perspectives. This study employs a cross-sectional and descriptive methodology to quantitatively analyze over 50026 tweets.  This study seeks to investigate how Twitter users seek and disseminate information related to cardiovascular diseases. It aims to identify the prevalent topics shared about cardiovascular diseases and analyze the content of these messages.  Initially, 50026 tweets from 8,619 users were gathered over a one-month timeframe. English tweets have been selected due to the prevalence of the English language. Subsequently, the tweets were categorized and analyzed utilizing the LDA technique and the MALLET platform. Content analysis was conducted across various categories, focusing on topics, temporal trends, and geographical locations of the tweets. The results show that there was a significant relationship between the parameters extracted in the research and the most concern of users was in the field of heart diseases and prevention methods. Most user tweets (36,323 or 72.60%) contained specific information about heart disease. 9.33% related to cardiovascular issues, 2817 (5.63%) tweets were about heart attack, 2949 (5.89%) were about heart failure and 3267 (6.5%) about other cases related to heart disorders (cardiac arrest, cardiomyopathy, ischemic heart, etc.). The most concern of users in the group of heart diseases was related to the connection of topics such as cholesterol (4102 tweets (11.04%)), prevention (20348 tweets (56.01%)) and diet (1114 (3.06%)) with heart disease.