scholarly journals Social media data as a proxy for hourly fine-scale electric power consumption estimation

2018 ◽  
Vol 50 (8) ◽  
pp. 1553-1557 ◽  
Author(s):  
Chengbin Deng ◽  
Weiying Lin ◽  
Xinyue Ye ◽  
Zhenlong Li ◽  
Ziang Zhang ◽  
...  
Keyword(s):  
Social Media ◽  
Power Consumption ◽  
Electric Power ◽  
Human Activity ◽  
Electric Power Consumption ◽  
Fine Scale ◽  
Human Beings ◽  
Social Media Data ◽  
Building Level ◽  
Media Data

Accurate forecasting of electric demand is essential for the operation of modern power system. Inaccurate load forecasting will considerably affect the power grid efficiency. Forecasting the electric demand for a small area, such as a building, has long been a well-known challenge. In this research, we examined the association between geotagged tweets and hourly electric consumption at a fine scale. All available geotagged tweets and electric meter readings were retrieved and spatially aggregated to each building in the study area. Comparing to traditional studies, the usage of geotagged tweets is to reflect human activity dynamics to some degree by considering human beings as sensors, which therefore can be employed at the building level. High correlation is found between the human activity indicator and the power consumption as supported by a correlation coefficient level over 0.8. To the best of our knowledge, rare studies placed an emphasis on hourly electric power consumption using social media data, especially at such a fine scale. This research shows the great potential of using Twitter data as a proxy of human activities to model hourly electric power consumption at the building level. More studies are warranted in the future to further examine the effectiveness of the proposed method in this research.

scholarly journals The use of biblical themes in the debate concerning the xenophobic attacks in South Africa

2015 ◽  
Vol 36 (1) ◽  
Author(s):  
Zorodzai Dube
Keyword(s):  
South Africa ◽  
Social Media ◽  
Ethical Issues ◽  
Public Image ◽  
Human Beings ◽  
Social Media Data ◽  
The Social ◽  
Use Of Social Media ◽  
Media Data ◽  
Economic Outlook

The study draws from the ideas of J�rgen Habermas, Daniel Trotter and Christian Fuchs, Zizi Papacharissis, Yochai Benkler and Christian Fuchs to investigate the use of social media as a platform to express ideas against xenophobic-related attacks in South Africa (April 2015�May 2015). The data was collected from twitter, YouTube and Facebook. Most views came from the Facebook platform called �Stop xenophobia�. Using ATLAS.ti, software for qualitative research, the data was coded into interpretive variables or categories. The results show that themes such as hospitality, morality, creation and ethics received highest frequency as reasons to condemn xenophobia. The research further reveals that the social media data is much candid in comparison to state controlled media, where views and ideas were censored to protect the economic and public image of the country. Unlike the controlled government outlets which focus on the possible correlation between xenophobic attacks to economic outlook, the social media focuses on moral and ethical issues � issues that define our collective as human beings and tackles xenophobia from the perspective of ethics and shared human values.Intradisciplinary and/or interdisciplinary implications: This study is interdisciplinary in nature due to the use of theories in media studies and social sciences to investigate the use of biblical themes in the fight against xenophobia.

Mapping fine-scale urban housing prices by fusing remotely sensed imagery and social media data

2018 ◽  
Vol 22 (2) ◽  
pp. 561-581 ◽  
Author(s):  
Yao Yao ◽  
Jinbao Zhang ◽  
Ye Hong ◽  
Haolin Liang ◽  
Jialv He
Keyword(s):  
Social Media ◽  
Housing Prices ◽  
Remotely Sensed ◽  
Urban Housing ◽  
Fine Scale ◽  
Social Media Data ◽  
Remotely Sensed Imagery ◽  
Media Data

Twitter-Mediated Enterprise–Customer Communication: Case of Electricity Distribution Services in a Developing Country

2021 ◽  
pp. 089443932110195
Author(s):  
Adeola O. Opesade
Keyword(s):  
Social Media ◽  
Electric Power ◽  
Customer Service ◽  
Power Distribution ◽  
Social Media Data ◽  
Electric Power Supply ◽  
Distribution Services ◽  
Electric Power Distribution ◽  
Service Operation ◽  
Media Data

Studies have shown that electric power supply failures can induce customers’ use of media for electric power–related communications. Nigeria is a country with considerably active users of social media but also with incessant electric power outages. However, no known study has been carried out on Nigeria’s electric power–related communications based on social media data. The present study investigated comparatively, the behaviors of companies and customers, in their use of Twitter for enterprise–customer communication on electric power distribution services in Nigeria. Using the data-driven science methods, the study revealed that both companies and customers use Twitter to disseminate information on electric power distribution in Nigeria. Companies’ corpora feature higher percentages of retweets while customers’ corpora feature higher percentages of direct public responses (@replies). The study also revealed a disjoint in the expectations of the companies and customers in their use of Twitter for communicating electric power distribution matters. While companies appear to leverage on the information sharing ability of the medium, customers appear to perceive it as a tool for accessing improved service delivery. The study recommends that Nigeria’s electric power distribution companies should incorporate Twitter into the customer service operation of their companies. This will enable information to get to the set of people who will process customers’ complaints as soon as possible.

scholarly journals Capturing and Characterizing Human Activities Using Building Locations in America

2019 ◽  
Vol 8 (5) ◽  
pp. 200 ◽  
Author(s):  
Ren ◽  
Jiang ◽  
Seipel
Keyword(s):  
Social Media ◽  
Big Data ◽  
Power Law ◽  
Human Activities ◽  
Statistical Distribution ◽  
Human Beings ◽  
Spatial Clusters ◽  
Social Media Data ◽  
Positioning Systems ◽  
Media Data

Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry.

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