scholarly journals A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

Author(s):  
T Heena Fayaz

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

2018 ◽  
Vol 57 (8) ◽  
pp. 2085-2109 ◽  
Author(s):  
Noor Al-Qaysi ◽  
Norhisham Mohamad-Nordin ◽  
Mostafa Al-Emran

The study of social media acceptance and adoption is not a new research topic. However, the analysis of the educational and information systems (IS) theories/models that are used to examine the social media acceptance and adoption is considered an important research direction. To examine these theories/models and provide researchers with a clear vision of this research topic, we should be aware of the leading educational and IS theories/models used in this line of research. To this end, this systematic review retrieved and analyzed 2,382 articles. The retrieved articles were then critically examined to meet the inclusion and exclusion criteria, in which 122 articles published between 2009 and 2018 were eventually selected for further critical analysis. The main findings indicated that the uses and gratifications theory (U&G) and the social constructivism theory were considered the most widely used educational theories in social media. Besides, the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) were considered the most extensively used IS models in studying the social media acceptance and adoption. These results afford a better understanding of social media studies related to the educational and IS theories/models and form a constructive reference for future research.


Author(s):  
Ayushi Aggarwal

Sentiment Analysis and summarization has a large number of application that are useful for determining the sentiment of the text and summarizing a big text into a small paragraph of few lines. Thus it has become an important topic to work on and for fulling the requirements of the customer. It has also become an important topic for researchers to focus on, as it is highly demanded and beneficial in different fields of product, services and growth of the business. At present when 89.9% of people are using social media platform, they express their reviews, feelings, emotions and share their comments and some exciting activities of their life through social media platform, so it becomes very important to analyses them and classify them as positive or negative, this can be done with the help of sentiment analysis. Also, to find the summary of a big document with large amount of data summarizer is very useful as we can get the summary of a document in the favorable number of line. The basic model of sentimental analysis classify the word as positive as negative with the help of some machine learning approaches, which will help in improving the quality of product and providing the service to the customer for building up a healthy competition in market and keeping the goodwill of the business . It also displays the output in the form of graph whose data is taken from social media platform. Sentimental analysis also helps in getting the summary of the document by picking the lines containing the words having maximum repetitions. It has been found that sentiment analysis able to classify the positive sentence by giving the output as 1 and negative sentences as 0. The model which is being built also graphically represents the classifications of positive and negative words picked from the dataset and it’s also useful in summarizing a document Thus sentiment analysis comes out to be very important for classify the unstructured data on social media platform and so there is always a scope of building a better model which is more accurate and efficient.


2022 ◽  
Vol 2022 ◽  
pp. 1-19
Author(s):  
Jetli Chung ◽  
Jason Teo

The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.


Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.


2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.


2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.


Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1872
Author(s):  
Justin Dela Cruz ◽  
David Kahan

Protein intake is an important factor for augmenting the response to resistance training in healthy individuals. Although food intake can help with anabolism during the day, the period of time during sleep is typically characterized by catabolism and other metabolic shifts. Research on the application of nighttime casein protein supplementation has introduced a new research paradigm related to protein timing. Pre-sleep casein supplementation has been attributed to improved adaptive response by skeletal muscle to resistance training through increases in muscle protein synthesis, muscle mass, and strength. However, it remains unclear what the effect of this nutritional strategy is on non-muscular parameters such as metabolism and appetite in both healthy and unhealthy populations. The purpose of this systematic review is to understand the effects of pre-sleep casein protein on energy expenditure, lipolysis, appetite, and food intake in both healthy and overweight or obese individuals. A systematic review following PRISMA guidelines was conducted in CINAHL, Cochrane, and SPORTDiscus during March 2021, and 11 studies met the inclusion criteria. A summary of the main findings shows limited to no effects on metabolism or appetite when ingesting 24–48 g of casein 30 min before sleep, but data are limited, and future research is needed to clarify the relationships observed.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Matt X. Richardson ◽  
Maria Ehn ◽  
Sara Landerdahl Stridsberg ◽  
Ken Redekop ◽  
Sarah Wamala-Andersson

Abstract Background Nocturnal digital surveillance technologies are being widely implemented as interventions for remotely monitoring elderly populations, and often replace person-based surveillance. Such interventions are often placed in care institutions or in the home, and monitored by qualified personnel or relatives, enabling more rapid and/or frequent assessment of the individual’s need for assistance than through on-location visits. This systematic review summarized the effects of these surveillance technologies on health, welfare and social care provision outcomes in populations ≥ 50 years, compared to standard care. Method Primary studies published 2005–2020 that assessed these technologies were identified in 11 databases of peer-reviewed literature and numerous grey literature sources. Initial screening, full-text screening, and citation searching steps yielded the studies included in the review. The Risk of Bias and ROBINS-I tools were used for quality assessment of the included studies. Result Five studies out of 744 identified records met inclusion criteria. Health-related outcomes (e.g. accidents, 2 studies) and social care outcomes (e.g. staff burden, 4 studies) did not differ between interventions and standard care. Quality of life and affect showed improvement (1 study each), as did economic outcomes (1 study). The quality of studies was low however, with all studies possessing a high to critical risk of bias. Conclusions We found little evidence for the benefit of nocturnal digital surveillance interventions as compared to standard care in several key outcomes. Higher quality intervention studies should be prioritized in future research to provide more reliable evidence.


2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.


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