scholarly journals The Influence of Individual Characteristics on Cultural Consumption from the Perspective of Complex Social Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hui Liu ◽  
Shuang Lu ◽  
Ximeng Wang ◽  
Shaobo Long
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Age Groups ◽  
Personal Characteristics ◽  
The Elderly ◽  
Individual Characteristics ◽  
Cultural Consumption ◽  
Communication Costs ◽  
Consumption Decision ◽  
The Impact

In the era of the digital economy, social network as an important social capital has an important influence on individual consumption decision-making. This article uses the latest data from the China Household Finance Survey (CHFS) in 2017 to analyze the impact of personal characteristics on cultural consumption behavior under the influence of social networks from a theoretical and empirical perspective. Studies have shown that (1) social networks have a significant impact on cultural consumption; compared to gift money and social interaction, communication costs have a greater impact on cultural consumption; (2) communication costs have a greater impact on education consumption, entertainment consumption, and tourism consumption; (3) under the influence of social networks, individual characteristics have a significant impact on cultural consumption; (4) the higher the level of education, the easier it is for cultural consumption. There are intergenerational differences in cultural consumption expenditures of different age groups. It is easier for people to consume entertainment than the elderly.

scholarly journals Quantifying the impact of air pollution on the urban population of Brazil

2007 ◽  
Vol 23 (suppl 4) ◽  
pp. S529-S536 ◽  
Author(s):  
Izabel Marcilio ◽  
Nelson Gouveia
Keyword(s):  
Air Pollution ◽  
Urban Population ◽  
Hospital Admissions ◽  
Age Groups ◽  
The Elderly ◽  
Impact Factors ◽  
Pollution Levels ◽  
Time Series Studies ◽  
The Impact ◽  
Respiratory Causes

This study aimed to quantify air pollution impact on morbidity and mortality in the Brazilian urban population using locally generated impact factors. Concentration-response coefficients were used to estimate the number of hospitalizations and deaths attributable to air pollution in seven Brazilian cities. Poisson regression coefficients (beta) were obtained from time-series studies conducted in Brazil. The study included individuals 65 years old and over and children under five. More than 600 deaths a year from respiratory causes in the elderly and 47 in children were attributable to mean air pollution levels, corresponding to 4.9% and 5.5% of all deaths from respiratory causes in these age groups. More than 4,000 hospital admissions for respiratory conditions were also attributable to air pollution. These results quantitatively demonstrate the currently observed contribution of air pollution to mortality and hospitalizations in Brazilian cities. Such assessment is thought to help support the planning of surveillance and control activities for air pollution in these and similar areas.

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

scholarly journals Personality and the Impact of Celebrity Endorsements on Instagram

2021 ◽  
Vol 18 (3) ◽  
pp. 468-474
Author(s):  
Yair Amichai-Hamburger ◽  
Shir Etgar ◽  
Hadar Gil-Ad ◽  
Michal Levitan-Giat ◽  
Gaya Raz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Locus Of Control ◽  
Strong Influence ◽  
Online Social Network ◽  
Strong Impact ◽  
Celebrity Endorsements ◽  
Internal Locus Of Control ◽  
The Impact ◽  
The Relationship

Celebrities are famous people who often belong to entertainment industry. They are known to have a strong influence on people’s behavior. In the digital age this impact has expanded to include the online arena. Celebrities increasingly utilize Instagram, an online social network, to promote commercial products. It is important to learn to what extent people are influenced by this type of promotion and what sort of people are likely to be swayed by it. Research has demonstrated that people’s personalities have a strong impact on their behaviors online. However, until now, these investigations have not included the relationship between personality and the degree of celebrity influence through social networks. This study examines how much the personality of a user is related to the degree to which he or she is influenced by these Celebrity Instagram messages. Participants comprised 121 students (34 males, 87 females). They answered questionnaires which focused on their personality and were asked about the degree of influence celebrities exerted upon them through Instagram. Results showed that people who are characterized as being open and having an internal locus of control are more resistant to such celebrity influences. This paper demonstrates that the personality of a recipient is likely to influence the degree of impact that a celebrity endorsement is likely to produce. The implications of these results are discussed.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals LIFE ON DISPLAY: WHY IT IS IMPORTANT TO EXPOSE YOUR ACTIONS ON THE SOCIAL NETWORK

2021 ◽  
Vol 6(72) (4) ◽  
pp. 41-48
Author(s):  
A. E. Starchenko ◽  
M. V. Semina
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Social Network ◽  
Reference Group ◽  
Human Life ◽  
Sociological Study ◽  
The Social ◽  
Personal Accounts ◽  
The Impact ◽  
To Receive

Social networks have emerged relatively recently in human life, but have already become an integral part of it. Companies tell about themselves, their activities, innovations, promotions and events in their profiles. This helps increase audience coverage, tell more about your brand, products, services. People in personal accounts have the opportunity to share their lives and creativity through photos, videos and texts. Now it is not necessary to receive higher education to become an operator, director or actor whose talent is recognized by society. It is enough to start a page on the social network and start sharing your knowledge and creativity. To find out why people post photos, videos and write texts on their social networks, a pilot sociological study was carried out. The method of deep interview with active users of social networks was chosen to carry out the study. The interview allowed getting unique information, to learn the opinion of users about social networks, the impact of the new way of communication on their life, to identify the reasons why users start and maintain profiles. The respondents were 20 users of social networks between the ages of 19 and 22. Interviewees have profiles on the most popular Instagram and Vkontakte networks. As a result of the analysis of the interview, a tendency was revealed to differ in the perception of users of their actions on the social network and similar actions of other users. Their content is perceived by them as opportunities to be in sight, as a resource to form their social status and an element of influence on their reference group. And the same content published by others is perceived as boasting.

The Impact of Social Networks on Science Students' Academic Performance in Ondo State Senior Secondary Schools, Nigeria

2021 ◽  
pp. 50-63
Author(s):  
Jethro Oludare OLOJO
Keyword(s):  
Social Networks ◽  
Academic Performance ◽  
Social Network ◽  
Science Students ◽  
The Social ◽  
Ondo State ◽  
Research Questions ◽  
Senior Secondary Schools ◽  
Senior Secondary ◽  
The Impact

The objective of this study was to examine the impact of social network usage on science students’ academic achievements in Ondo State’s senior secondary schools. The study was also to find the extent to which students under investigation used the social network platforms and the frequencies of their visits. In order to achieve this, a structured questionnaire was designed and administered to students from the three senatorial districts that made up the state. A multistage; which involved simple random and purposive sampling approaches was used to select the sample for the study. 150 copies of the questionnaire were distributed; out of which, 148 (98.78%) copies were returned. For the study, four research questions and two research hypotheses were developed. The hypotheses were assessed using the student's - t statistic at 0.05 significant level; using SPSS version 20 while the research questions formulated were evaluated using frequency counts and percentages. The study revealed that Ondo State senior secondary school science students can efficiently use the social network platforms for academic activities with male students being more proficient than their female counterparts. The study also revealed that the usage of social networks has assisted students to improve their academic performance; irrespective of their classes. Besides, the study showed that Facebook was the most popular of all the social network platforms. To this end, the researcher recommended that teachers, parents, and guidance should monitor the activities of their wards on the social network sites so that they can use the platforms to benefit their lots. Teachers should also use the advantage of students’ exposure to social networking to change their teaching methods from traditional one to online teaching.

scholarly journals The Impact of Cold and Heat on Years of Life Lost in a Northwestern Chinese City with Temperate Continental Climate

2019 ◽  
Vol 16 (19) ◽  
pp. 3529 ◽  
Author(s):  
Jiangtao Liu ◽  
Yueling Ma ◽  
Yuhong Wang ◽  
Sheng Li ◽  
Shuyu Liu ◽  
...  
Keyword(s):  
Respiratory Diseases ◽  
Heat Waves ◽  
Age Groups ◽  
The Elderly ◽  
Years Of Life Lost ◽  
Weather Variables ◽  
Distributed Lag ◽  
Heat Effects ◽  
The Impact ◽  
Continental Climate

Cold spells and heat waves in a changing climate are well known as great public-health concerns due to their adverse effects on human health. However, very few studies have quantified health impacts of heat and cold in the region of Northwestern China. The purpose of the present study was to evaluate the effects of cold and heat on years of life lost (YLL) in Lanzhou, a city with temperate continental climate. We compiled a daily dataset including deaths, weather variables, and air pollutants in Lanzhou, China, from 2014–2017. We used a distributed lag non-linear model to estimate single-day and cumulative effects of heat and cold on daily YLL. Results indicated that both cold and heat were associated with increased YLL for registered residents in Lanzhou. Estimated heat effects appeared immediately in the first two days, while estimated cold effects lasted over a longer period (up to 30 days). Cold significantly increased the YLL of all residents except for males and those with respiratory diseases (≥65 years). Our results showed that both heat and cold had more pronounced effects on cardiovascular diseases compared to respiratory diseases. Males might be more vulnerable to heat, while females might suffer more YLL from cold. The effects of cold or heat on the elderly might appear earlier and last longer than those for other age groups.

scholarly journals Age sequences of the elderly’ social network and its efficacies on well-being: an urban-rural comparison in China

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenhua Zheng ◽  
Hong Chen
Keyword(s):  
Social Network ◽  
Rural Areas ◽  
Age Groups ◽  
Well Being ◽  
The Elderly ◽  
Urban Elderly ◽  
Family Network ◽  
The Social ◽  
Urban Rural ◽  
Urban And Rural Areas

Abstract Background Although social network is a known determinant of the elderly’s well-being, it is not clear, in urban-rural and age-comparison, what its structural characteristics are and how it works for well-being. The research aims to discuss the features of the elderly’s social network and the social network efficacies on the well-being of older adults in China’s urban and rural areas as well as revealing the urban-rural disparities among the elderly of different age groups. Methods In this study, descriptive statistical analysis and structural equation Modeling (SEM) were used to make a group comparison between the urban and rural elderly of different age groups. All data are quoted from 2014 China Longitudinal Aging Social Survey (CLASS). The survey adopted the multi-stage probability sampling method, targeting Chinese senior citizens aged 60 and above, the ultimate samples totaled 11,511. Results The social network of the elderly in China feature a “reverse structure” in age sequences: with ageing, family network of the elderly expand while their friend network shrink; also, the expansion scale of the rural elderly’s family network is significantly larger than that of the city’s while the shrinkage scale of their friend network is smaller compared with its urban counterpart. The effect of family network on the rural elderly’s well-being shows a remarkable increase with age. However, there is no noticeable change in urban elderly groups of different ages. Conclusion The social network characteristics of the Chinese elderly are different between different age stages. Namely, the family network and the friend network have the “reverse structure “ in age sequences. Meanwhile, the family network and the friend network have different efficacies on the well-being of the elderly in China, and the differences between urban and rural areas are even more obvious. For rural elderly, family network has very important effects on their well-being. Moreover, With the increase of age, family network’s efficacies increase gradually. For urban elderly, comparatively, family network is just as important as friend network.

scholarly journals The Impact of Non-optimum Ambient Temperature on Years of Life Lost: A Multi-county Observational Study in Hunan, China

2020 ◽  
Vol 17 (8) ◽  
pp. 2699
Author(s):  
Ling-Shuang Lv ◽  
Dong-Hui Jin ◽  
Wen-Jun Ma ◽  
Tao Liu ◽  
Yi-Qing Xu ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Ambient Temperature ◽  
Respiratory Disease ◽  
Age Groups ◽  
The Elderly ◽  
Cold Temperature ◽  
Years Of Life Lost ◽  
Distributed Lag ◽  
Preventative Measures ◽  
The Impact

The ambient temperature–health relationship is of growing interest as the climate changes. Previous studies have examined the association between ambient temperature and mortality or morbidity, however, there is little literature available on the ambient temperature effects on year of life lost (YLL). Thus, we aimed to quantify the YLL attributable to non-optimum ambient temperature. We obtained data from 1 January 2013 to 31 December 2017 of 70 counties in Hunan, China. In order to combine the effects of each county, we used YLL rate as a health outcome indicator. The YLL rate was equal to the total YLL divided by the population of each county, and multiplied by 100,000. We estimated the associations between ambient temperature and YLL with a distributed lag non-linear model (DNLM) in a single county, and then pooled them in a multivariate meta-regression. The daily mean YLL rates were 22.62 y/(p·100,000), 10.14 y/(p·100,000) and 2.33 y/(p·100,000) within the study period for non-accidental, cardiovascular, and respiratory disease death. Ambient temperature was responsible for advancing a substantial fraction of YLL, with attributable fractions of 10.73% (4.36–17.09%) and 16.44% (9.09–23.79%) for non-accidental and cardiovascular disease death, respectively. However, the ambient temperature effect was not significantly for respiratory disease death, corresponding to 5.47% (−2.65–13.60%). Most of the YLL burden was caused by a cold temperature than the optimum temperature, with an overall estimate of 10.27% (4.52–16.03%) and 15.94% (8.82–23.05%) for non-accidental and cardiovascular disease death, respectively. Cold and heat temperature-related YLLs were higher in the elderly and females than the young and males. Extreme cold temperature had an effect on all age groups in different kinds of disease-caused death. This study highlights that general preventative measures could be important for moderate temperatures, whereas quick and effective measures should be provided for extreme temperatures.

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