Kernel Optimized Regression Model for Product Recommendation Connecting Social media to E-Commerce

2018 ◽  
Vol 6 (5) ◽  
pp. 527-536
Author(s):  
Lija John
Keyword(s):  
Social Media ◽  
Regression Model ◽  
Product Recommendation

scholarly journals Social Media Crisis Communication Model for Building Public Resilience: A Preliminary Study

2021 ◽  
pp. 245-256
Author(s):  
Umar Ali Bukar ◽  
Marzanah A Jabar ◽  
Fatimah Sidi ◽  
Rozi Nor Haizan Nor ◽  
Salfarina Abdullah
Keyword(s):  
Social Media ◽  
Regression Model ◽  
Crisis Communication ◽  
Preliminary Investigation ◽  
Crisis Response ◽  
Kappa Statistics ◽  
Communication Model ◽  
Dynamic Feature ◽  
Consistency Requirement ◽  
Process Macro

There is an ongoing discussion about the effectiveness of social media usage on the ability of people to recover from the crisis. However, the existing social media crisis communication models could not address the dynamic feature of social media users and the crisis, respectively. Therefore, the objective of this study is to conduct a preliminary investigation of the social media crisis communication model for building public resilience. Thus, 34 items were generated from the literature concerning the crisis, crisis response, social interaction, and resilience. The items were validated by three experts via content validity index and modified kappa statistics. After passing the validation test, the instruments were pre-tested by 32 participants. The reliability of the items was analyzed using Cronbach’s alpha. Also, the model fits and mediation were examined by the regression model, and the hypotheses were independently assessed in process macro models. Based on the result obtained, each of the constructs satisfied the internal consistency requirement; crisis (0.743), crisis response (0.724), social media interaction (0.716), and resilience (0.827). Furthermore, the result also indicates that the regression model is a good fit for the data. The independent variables statistically significantly predict the dependent variable, p < 0.05. Also, the result of the process macro models indicates that all the hypotheses are independently supported.

scholarly journals Unemployment, Personality Traits, and the Use of Facebook

2021 ◽  
Vol 13 (1) ◽  
pp. 56-72
Author(s):  
Dandison C. Ukpabi ◽  
Olayemi Olawumi ◽  
Oluwafemi Samson Balogun ◽  
Chijioke E. Nwachukwu ◽  
Sunday Adewale Olaleye ◽  
...  
Keyword(s):  
Social Support ◽  
Social Media ◽  
Regression Model ◽  
Personality Traits ◽  
Social Economic ◽  
Online Social Support ◽  
Unemployment Data ◽  
The Relationship ◽  
Practical Implications ◽  
The Way

Different personality traits respond differently to unfavourable life situations. Unemployment can have several negative social, economic, and domestic consequences. Many people use social media for a variety of reasons. The aim of this study is to examine the way different personality traits respond to Facebook in the period of unemployment. Data was obtained from 3,002 unemployed respondents in Nigeria. The study used regression model to analyse the data. Among the five personality traits, results indicated that the relationship between neuroticism and online social support was negative. However, the relationship between online social support and satisfaction was positive. The study highlights several theoretical and practical implications.

scholarly journals Factors predicting knowledge on COVID-19 misconceptions and perception of government efforts in Ghana: a cross-sectional study

2020 ◽  
Vol 6 (9) ◽  
pp. 340
Author(s):  
Emmanuel K. Bondah ◽  
Daniel O. Agyemang
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cross Sectional Study ◽  
World Health ◽  
Sectional Study ◽  
Cross Sectional ◽  
Level Of Knowledge ◽  
The Government

<p class="abstract"><strong>Background:</strong> With the increasing rate of COVID-19 cases and mortality across the globe, countries and most people have adopted precautionary and preventive measures to avoid been infected with the disease. However, several trending myths and misconceptions also floods the world during this era.</p><p class="abstract"><strong>Methods: </strong>A cross-sectional study was conducted among 587 participants (53% males, 78% aged 18-30 years, and 74% having at least tertiary level education) in Ghana, using a convenience snowballing sampling approach. A self-designed questionnaire based on “World Health Organization (WHO) myth-busters” was used for data collection. A logistic regression model was developed to explore variables predicting misconceptions.</p><p class="abstract"><strong>Results: </strong>With about 75% of respondents believing they knew “a lot” about coronavirus, 41% (confidence interval: 37-45) reported a high level of knowledge on the new coronavirus misconceptions. Social media (87%), and television/radio (57%) were the major sources of knowledge. Masters/Ph.D. degree education, National democratic congress (NDC) political members, other political party members, excellent self-health ratings, social media, respondents that anticipate a remedy in 1-5 years, and the perception that the government is “not doing enough” in fighting the pandemic, were reported factors predicting knowledge in a multiple logistic regression model (p&lt;0.05). Region of residence, political affiliation, self-health ratings, predicted time of remedy, and level of knowledge on covid-19 misconceptions were also associated with the perception of government efforts (p&lt;0.05).</p><p class="abstract"><strong>Conclusion: </strong>Most Ghanaians have much belief in trending misinformation related to the pandemic. Public health education and campaigns should address these misconceptions and encourage the public to seek information from credible sources.</p>

Investigating Deciding Factors of Product Recommendation in Social Media

2018 ◽  
pp. 249-257
Author(s):  
Jou Yu Chen ◽  
Ping Yu Hsu ◽  
Ming Shien Cheng ◽  
Hong Tsuen Lei ◽  
Shih Hsiang Huang ◽  
...  
Keyword(s):  
Social Media ◽  
Product Recommendation

scholarly journals Predictors of Anxiety and Depression in Medical Professionals During the Time of COVID-19 Outbreak

2021 ◽  
pp. 1-19
Author(s):  
Zhengjia Ren ◽  
Zhongyao Xie
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mental Health Disorder ◽  
Anxiety And Depression ◽  
Medical Professionals ◽  
Depression Symptoms ◽  
Sociodemographic Data

ABSTRACT Objectives: The aim of this study was to investigate the influences of sociodemographic data, mental disorder history, confusion and somatic discomfort triggered by social media on anxiety and depression symptoms among medical professionals during COVID-19 outbreak. Methods: 460 participants completed online questionnaires that included sociodemographic data, mental health disorder history, an assessment of confusion and somatic discomfort triggered by social media, and psychological disturbance. Hierarchical linear regression model was adopted to analysis the data. Results: The hierarchical linear regression model was able to explain 41.7% of variance in depression symptoms. Including: comorbidity with one mental disorder (B= 0.296, P < .001), confusion (B= 0.174, P < .001) and somatic discomfort (B=0.358, P<.001) triggered by social media. The hierarchical linear regression model was able to explain 41.7% of variance in anxiety symptoms, including: sex (B = -0.08, P < .005), comorbidity with one mental health disorder (B= 0.242, P < .001), confusion (B= 0.228, P < .001) and somatic discomfort (B=0.436, P<.001) triggered by social media. Conclusions: These results suggest that it is important to provide adequate psychological assistance for medical professionals with mental health problems in COVID-19 to buffer the negative impact of social media.

scholarly journals Using text analytics to measure an effect of topics and sentiments on social-media engagement: Focusing on Facebook fan page of Toyota

2021 ◽  
Vol 13 ◽  
pp. 184797902110162
Author(s):  
Heuiju Chun ◽  
Byung-Hak Leem ◽  
Hyesun Suh
Keyword(s):  
Social Media ◽  
Regression Model ◽  
Topic Modeling ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Influential Factor ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
Social Media Engagement ◽  
The Media

In this study we investigate whether Facebook fan-page posting types and topics have a significant effect on engagement. More specifically, the media type and content theme of posting on Facebook are examined to see whether or not there was a difference between content topics. In order to achieve this goal, we set hypotheses as follows: (1) the media types of posting have a significant effect on engagement; (2) the topics and sentiment polarity of posting have a significant effect on engagement. We tested these hypotheses using research procedures as follows: (1) collection and preprocessing of social-media data, including posting types, comments, and reactions on Facebook fan pages, (2) topic modeling of fan-page postings using R and SAS, (3) testing hypotheses using a negative binomial regression model, and (4) implications and insights for social-media marketing. Topic modeling applying to textual data and sentiment analysis were conducted. After that, in order to find the factors to affect the number of Facebook fan-page engagements, the negative binomial regression model including post type, topic, sentiment, reactions of “love,” “haha,” and their interaction as exploratory variables was considered. Finally, the results show that post type is the most influential factor to affect social-media engagement, and content topics, sentiments of posts and comments also have significant effects on it.

scholarly journals Exploring demographic information in social media for product recommendation

2015 ◽  
Vol 49 (1) ◽  
pp. 61-89 ◽  
Author(s):  
Wayne Xin Zhao ◽  
Sui Li ◽  
Yulan He ◽  
Liwei Wang ◽  
Ji-Rong Wen ◽  
...  
Keyword(s):  
Social Media ◽  
Demographic Information ◽  
Product Recommendation

scholarly journals A Peer-Led Social Media Intervention to Improve Interest in Research Careers Among Urban Youth: Mixed Methods Study

10.2196/16392 ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e16392
Author(s):  
Christianah Ogunleye ◽  
Jeanne M Farnan ◽  
Shannon K Martin ◽  
Audrey Tanksley ◽  
Samantha Ngooi ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Clinical Research ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Scientific Research ◽  
Ordinal Logistic Regression ◽  
Minority Youth ◽  
Research Careers ◽  
Ordinal Logistic Regression Model

Background Novel methods to boost interest in scientific research careers among minority youth are largely unexplored. Social media offers a unique avenue toward influencing teen behavior and attitudes, and can therefore be utilized to stimulate interest in clinical research. Objective The aim of this study was to engage high-achieving minority youth enrolled in a science pipeline program to develop a targeted social media marketing campaign for boosting interest in clinical research careers among their peers. Methods Students enrolled in the Training Early Achievers for Careers in Health program conducted focus groups in their communities to inform themes that best promote clinical research. They then scripted, storyboarded, and filmed a short video to share on social media with a campaign hashtag. Additionally, each student enrolled peers from their social circle to be subjects of the study. Subjects were sent a Career Orientation Survey at baseline to assess preliminary interest in clinical research careers and again after the campaign to assess how they saw the video, their perceptions of the video, and interest in clinical research careers after watching the video. Subjects who did not see the video through the online campaign were invited to watch the video via a link on the postsurvey. Interest change scores were calculated using differences in Likert-scale responses to the question “how interested are you in a career in clinical research?” An ordinal logistic regression model was used to test the association between watching a peer-shared video, perception of entertainment, and interest change score controlling for underrepresented minorities in medicine status (Black, American Indian/Alaska Native, Native Hawaiian, or Pacific Islander), gender, and baseline interest in medical or clinical research careers. Results From 2014 to 2017, 325 subjects were enrolled as part of 4 distinct campaigns: #WhereScienceMeetsReality, #RedefiningResearch, #DoYourResearch, and #LifeWithoutResearch. Over half (n=180) of the subjects watched the video via the campaign, 227/295 (76.9%) found the video entertaining, and 92/325 (28.3%) demonstrated baseline interest in clinical research. The ordinal logistic regression model showed that subjects who viewed the video from a peer (odds ratio [OR] 1.56, 95% CI 1.00-2.44, P=.05) or found the video entertaining (OR 1.36, 95% CI 1.01-1.82, P=.04) had greater odds of increasing interest in a clinical research career. Subjects with a higher baseline interest in medicine (OR 1.55, 95% CI 1.28-1.87, P<.001) also had greater odds of increasing their interest in clinical research. Conclusions The spread of authentic and relevant peer-created messages via social media can increase interest in clinical research careers among diverse teens. Peer-driven social media campaigns should be explored as a way to effectively recruit minority youth into scientific research careers.

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