scholarly journals Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019 ◽  
Vol 11 (10) ◽  
pp. 2822 ◽  
Author(s):  
Rizwan Muhammad ◽  
Yaolong Zhao ◽  
Fan Liu
Keyword(s):  
Social Media ◽  
Social Network ◽  
Activity Patterns ◽  
Low Frequency ◽  
Spatiotemporal Analysis ◽  
Geographic Area ◽  
Primary Objective ◽  
Space And Time ◽  
Wide Range ◽  
Location Based Social Network

In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Study of Automotive Brands Popularity in Indonesia Using Twitter Data

2016 ◽  
Vol 3 (1) ◽  
pp. 23-33
Author(s):  
Stevent Efendi ◽  
Alva Erwin ◽  
Kho I Eng
Keyword(s):  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Automotive Industry ◽  
Real World ◽  
The Internet ◽  
Brand Preference ◽  
Twitter Data ◽  
Wide Range ◽  
Widespread Phenomenon

Social media has been a widespread phenomenon in the recent years. People shared a lot of thought in social media, and these data posted on the internet could be used for study and researches. As one of the fastest growing social network, Twitter is a particularly popular social media to be studied because it allows researchers to access their data. This research will look the correlation between Twitter chatter of a brand and the sales of brands in Indonesia. Factors such as sentiment and tweet rate are expected to be able to predict the popularity of a brand. Being one of the biggest industries in Indonesia, automotive industry is an interesting subject to study. A wide range of people buys vehicles, and even gather as communities based on their car or motorcycle brand preference. The Twitter results of sentiment analysis and tweet rate will be compared with real world sales results published by GAIKINDO and AISI.

Developing Marketing Strategy on Social Networks

2018 ◽  
pp. 66-82
Author(s):  
Boris Milović
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Nonprofit Organizations ◽  
Public Awareness ◽  
Winning Strategy ◽  
Marketing Performance ◽  
Wide Range ◽  
And Training

Social networks have proven to be very convenient and effective medium for the spreading of marketing messages, advertising, branding and promotion of products and services. Social networks offer companies, nonprofit organizations, political parties etc. sending certain messages for free. In addition, they allow companies access to a wide range of characteristics of their users. Developing appropriate, the winning strategy for marketing in social media is a comprehensive, time-intensive process therefore it is important to know to manage their content. Social networks transform certain classical approaches to marketing. They provide creative and relatively easy way to increase public awareness of the company and its products, and facilitate obtaining feedback and decision making. These are sources of different information about users and groups that they've joined. The success itself of marketing performance on a social network depends on the readiness and training of organizations to perform on them.

Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

2020 ◽  
Vol 9 (2) ◽  
pp. 137 ◽  
Author(s):  
Muhammad Rizwan ◽  
Wanggen Wan ◽  
Luc Gwiazdzinski
Keyword(s):  
Social Media ◽  
Movement Patterns ◽  
District Level ◽  
Smart Devices ◽  
Daily Routine ◽  
Space And Time ◽  
Directional Analysis ◽  
Wide Range ◽  
And Behavior ◽  
Urban Activities

Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as “geolocation”) in the form of a tweet (also referred to as a “check-in”), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user’s activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user’s daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time.

LBSN-Based Personalized Routes Recommendation

2014 ◽  
Vol 644-650 ◽  
pp. 3230-3234
Author(s):  
Li Chao Zhu ◽  
Zhi Jun Li ◽  
Shou Xu Jiang
Keyword(s):  
Social Network ◽  
Activity Pattern ◽  
Activity Patterns ◽  
Semantic Space ◽  
Location Based Social Network ◽  
Geographical Space ◽  
Similarity Method ◽  
Better Than

In this paper, we present personalized routes recommendation on Location Based Social Network. We model user in both geographical space and semantic space, and define Activity Pattern to describe individual’s personalized character, i.e. individual’s activity regularity. We extract routes which match individual’s activity patterns from high similar users’ trajectories, and according to scoring strategy to recommend top-k routes to a user. We evaluated our method with a real GPS dataset collected from GeoLife. The results show that there exist Activity Pattern in individual’s movement, and our method is better than traditional Cosine-based Similarity method on both precision and k-cover.

scholarly journals Sentiment Severity on Location-Based Social Network (LBSN) Data of Natural disasters

2020 ◽  
Vol 8 (5) ◽  
pp. 4219-4224
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Twitter Data ◽  
Location Based Social Network ◽  
Negative Sentiment ◽  
Disaster Event ◽  
Extract Information

Social media emerged as one of the key components to reach disaster affected people, as they supplement planning and operational coordination. Sentiment analysis was expended to identify, extract or characterize subjective information, such as opinions, expressed in a tweet. The sentiment expressed is analyzed and is classified as positive or negative sentiment, which is not versatile enough to capture the exact sentiment conveyed by the user. Opinion mining is a machine learning process used to extract information conveyed by the user in the form of text. In this paper, the lexical analysis to sentiment analysis of twitter data is employed. Conventionally, the sentiment is conveyed using the polarity of the data but in this paper, sentiment intensity is employed to convey the sentiments. Performing sentiment analysis on tweets gives us the sentiment intensity conveyed by the user, which in turn is used to calculate the severity of the disaster event specified by the user. Further, it is also used to classify the tweets based on their severity. This paper proposes a methodology to extract relevant sentiment information from Location Based Social Network (LBSN) and suggests a unique scale to classify this information to help disaster management authority.

scholarly journals A motivational perspective on the user acceptance of social media

2016 ◽  
Vol 47 (4) ◽  
pp. 1-13 ◽  
Author(s):  
A. Nelmapius ◽  
C. Boshoff
Keyword(s):  
Social Media ◽  
Social Network ◽  
Information Dissemination ◽  
Social Network Sites ◽  
Essential Element ◽  
Primary Objective ◽  
Perceived Usefulness ◽  
Message Content ◽  
The Future ◽  
Media Message

Social media is a unique marketing communication medium to engage with a new generation of consumers and it has become an essential element of many organisations’ strategic planning. On social media sites, consumers are engaging with and producing information, as opposed to traditional media where the marketer is in control of the media message content and information dissemination. The primary objective of this study was to investigate the intentions of users of social network sites to continue using social network sites in the future, by using a comprehensive, decomposed Theory of Planned Behaviour. The results showed that Dispositional trust, Internet self-efficacy, Psychological risk, Perceived enjoyment and Perceived usefulness exert a statistically significant influence on the intention of individuals to continue to use Facebook in the future. This study provides insights that can guide marketers’ efforts to devise customised, multi-layered marketing offerings to encourage the use of social network sites for e-commerce purposes.

scholarly journals A Social Network Analysis of Tweets Related to Masks during the COVID-19 Pandemic

2020 ◽  
Vol 17 (21) ◽  
pp. 8235
Author(s):  
Wasim Ahmed ◽  
Josep Vidal-Alaball ◽  
Francesc Lopez Segui ◽  
Pedro A. Moreno-Sánchez
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Communication Channel ◽  
The Public ◽  
Twitter Data ◽  
Wide Range ◽  
Media Research ◽  
Systematic Random Sample

Background: High compliance in wearing a mask is a crucial factor for stopping the transmission of COVID-19. Since the beginning of the pandemic, social media has been a key communication channel for citizens. This study focused on analyzing content from Twitter related to masks during the COVID-19 pandemic. Methods: Twitter data were collected using the keyword “mask” from 27 June 2020 to 4 July 2020. The total number of tweets gathered were n = 452,430. A systematic random sample of 1% (n = 4525) of tweets was analyzed using social network analysis. NodeXL (Social Media Research Foundation, California, CA, USA) was used to identify users ranked influential by betweenness centrality and was used to identify key hashtags and content. Results: The overall shape of the network resembled a community network because there was a range of users conversing amongst each other in different clusters. It was found that a range of accounts were influential and/or mentioned within the network. These ranged from ordinary citizens, politicians, and popular culture figures. The most common theme and popular hashtags to emerge from the data encouraged the public to wear masks. Conclusion: Towards the end of June 2020, Twitter was utilized by the public to encourage others to wear masks and discussions around masks included a wide range of users.

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