scholarly journals How does hand gestures in videos impact social media engagement - Insights based on deep learning

2021 ◽  
Vol 1 (2) ◽  
pp. 100036
Author(s):  
Kartik Anand ◽  
Siddhaling Urolagin ◽  
Ram Krishn Mishra
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Hand Gestures ◽  
Social Media Engagement

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

Social media engagement, service complexity, and experiential quality in US hospitals

2020 ◽  
Vol 34 (6) ◽  
pp. 833-845 ◽  
Author(s):  
Youngsu Lee ◽  
Joonhwan In ◽  
Seung Jun Lee
Keyword(s):  
Social Media ◽  
Demographic Data ◽  
Positive Association ◽  
Customer Engagement ◽  
Service Firms ◽  
Multiple Sources ◽  
Cross Sectional ◽  
Content Type ◽  
Social Media Engagement ◽  
Experiential Quality

Purpose As social media platforms become increasingly popular among service firms, many US hospitals have been using social media as a means to improve their patients’ experiences. However, little research has explored the implications of social media use within a hospital context. The purpose of this paper is to investigate a hospital’s customer engagement through social media and its association with customers’ experiential quality. Also, this study examines the role of a hospital’s service characteristics, which could shape the nature of the interactions between patients and the hospital. Design/methodology/approach Data from 669 hospitals with complete experiential quality and demographic data were collected from multiple sources of secondary data, including the rankings of social media friendly hospitals, the Hospital Compare database, the Center for Medicare and Medicaid (CMS) cost report, the CMS impact file, the Healthcare Information and Management Systems Society Analytics database and the Dartmouth Atlas of Health Care. Specifically, the authors designed the instrumental variable estimate to address the endogeneity issue. Findings The empirical results suggest a positive association between a hospital’s social media engagement and experiential quality. For hospitals with a high level of service sophistication, the association between online engagement and experiential quality becomes more salient. For hospitals offering various services, offline engagement is a critical predictor of experiential quality. Research limitations/implications A hospital with more complex services should make efforts to engage customers through social media for better patient experiences. The sample is selected from databases in the US, and the databases are cross-sectional in nature. Practical implications Not all hospitals may be better off improving the patient experience by engaging customers through social media. Therefore, practitioners should exercise caution in applying the study’s results to other contexts and in making causal inferences. Originality/value The current study delineates customer engagement through social media into online and offline customer engagement. This study is based on the theory of customer engagement and reflects the development of mobile technology. Moreover, this research may be considered as pioneering in that it considers the key characteristics of a hospital’s service operations (i.e., service complexity) when discovering the link between customers’ engagement through a hospital’s social media and experiential quality.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

Social media engagement for global influencers

2021 ◽  
pp. 1-15
Author(s):  
Kara Bentley ◽  
Charlene Chu ◽  
Cristina Nistor ◽  
Ekin Pehlivan ◽  
Taylan Yalcin
Keyword(s):  
Social Media ◽  
Social Media Engagement

scholarly journals A computational reward learning account of social media engagement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Björn Lindström ◽  
Martin Bellander ◽  
David T. Schultner ◽  
Allen Chang ◽  
Philippe N. Tobler ◽  
...  
Keyword(s):  
Social Media ◽  
Human Behavior ◽  
Computational Models ◽  
Average Rate ◽  
Human Life ◽  
Modern Human ◽  
Reward Learning ◽  
Learning Mechanisms ◽  
Social Media Engagement ◽  
Social Rewards

AbstractSocial media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

scholarly journals It takes two to tango: the interplay between decision logics, communication strategies and social media engagement in start-ups

2021 ◽  
Author(s):  
Christian Rudeloff ◽  
Stefanie Pakura ◽  
Fabian Eggers ◽  
Thomas Niemand
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Fuzzy Set ◽  
Exploratory Study ◽  
Communication Strategies ◽  
Qualitative Comparative Analysis ◽  
Communication Models ◽  
Social Media Engagement ◽  
Start Ups

AbstractThis manuscript analyzes start-ups’ usage of different communication strategies (information, response, involvement), their underlying decision logics (effectuation, causation, strategy absence) and respective social media success. A multitude of studies have been published on the decision logics of entrepreneurs as well as on different communication strategies. Decision logics and according strategies and actions are closely connected. Still, research on the interplay between the two areas is largely missing. This applies in particular to the effect of different decision logics and communication models on social media success. Through a combination of case studies with fuzzy-set Qualitative Comparative Analysis this exploratory study demonstrates that different combinations of causal and absence of strategy decision logics can be equally successful when it comes to social media engagement, whereas effectuation is detrimental for success. Furthermore, we find that two-way-communication is essential to create engagement, while information strategy alone cannot lead to social media success. This study provides new insights into the role of decision logics and connects effectuation theory with the communication literature, a field that has been dominated by causal approaches.

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