scholarly journals Using Semiotic Profiles to Design Graphical User Interfaces for Social Media Data Spaces on Mobile Phone Screens

2013 ◽  
Author(s):  
Andre Valdestilhas ◽  
Ansgar Scherp ◽  
Paulo Marcotti
Keyword(s):  
Social Media ◽  
Mobile Phone ◽  
User Interfaces ◽  
Graphical User Interfaces ◽  
Social Media Data ◽  
Media Data

A mixed-method approach for modelling customer-centric mobile phone reverse logistics: application of social media data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sajjad Shokouhyar ◽  
Amirhossein Dehkhodaei ◽  
Bahar Amiri
Keyword(s):  
Social Media ◽  
Big Data ◽  
Mobile Phone ◽  
Reverse Logistics ◽  
Significant Feature ◽  
Special Focus ◽  
Social Media Data ◽  
Content Type ◽  
Website Analysis ◽  
Media Data

Purpose Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantage and high efficiency by customers because of the expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm’s success. Therefore, paying attention to customer feedback in supply chain and logistics processes has recently increased so that manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumer purchasing behaviour and to analyse the interrelationship among them to develop a framework for customer-centric RL. These features are studied based on website analysis of several mobile phone manufacturers. The special focus of this paper is on social media data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach. Design/methodology/approach A portfolio of mobile phone features that affect consumer’s mobile phone purchasing decisions has been taken from website analysis by several mobile phone manufacturers to achieve this objective. Then, interrelationships between the identified features have been established by using big data supplemented with interpretive structural modelling (ISM). Apart from that, cross-impact matrix multiplication, applied to classification analysis, was carried out to graphically represent these features based on their driving power and dependence. Findings During the study, it has been observed from the ISM that the chip (F5) is the most significant feature that affects customer’s buying behaviour; therefore, mobile phone manufacturers realize that this is to be addressed first. Originality/value The focus of this paper is on social media data (Twitter) so that experts can understand the interaction between mobile phone features that affect consumer’s decisions on mobile phone purchasing by using the results.

scholarly journals Measurement of Potential Victims of Burglary at the Mesoscale: Comparison of Census, Phone Users, and Social Media Data

2021 ◽  
Vol 10 (5) ◽  
pp. 280
Author(s):  
Zhuofang Zhang ◽  
Lin Liu ◽  
Sisun Cheng
Keyword(s):  
Social Media ◽  
Mobile Phone ◽  
Rational Choice Theory ◽  
Choice Theory ◽  
Population Census ◽  
Social Media Data ◽  
Census Tracts ◽  
Small Areas ◽  
Time Period ◽  
Media Data

Since the target of burglars is generally the property of the inhabitant, it is crucial to accurately measure potential victims when analyzing burglaries, especially in small areas. Previous studies on burglary are mostly based on large units such as census tracts or communities. One of the difficulties is the measurement of the potential victims of burglary at the mesoscale. We compare the measuring effects of census population, census households, nighttime mobile phone users, and nighttime social media, such as the Tencent regional heatmap (TRH), on potential victims of burglary on 150 m × 150 m grids. Based on the rational choice theory, and controlling for the potentially confounding effects of risks and cost, we show that the TRH performed best, followed by census households and census population, and phone users performed poorly. The best-performing time period for TRH data was 3:00–5:00 am on weekends. These findings could lead to an improved measurement of potential victims of burglary at the mesoscale, and could provide scientific insight for crime prevention.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data
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