Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

Ostermann, Frank O.

doi:10.3390/ijgi10020052

Cited by 7 publications

(5 citation statements)

References 65 publications

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“…This suggestion helps to improve the generality power of Twitter studies investigating spatial factors by increasing the number of users whose location is identified. We also suggest researchers who are interested in linking Twitter data to external datasets to explore whether there is a significant correlation between Twitter data and external characteristics, such as socio-demographic factors, before further investigation [73].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Karami

Kadari

Panati

et al. 2021

IJGI

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Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population.

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Section: Discussionmentioning

confidence: 99%

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Karami

Kadari

Panati

et al. 2021

IJGI

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“…It is essential to note that, while this dataset is as complete as possible given the available Twitter data, geotagged Twitter data is not necessarily representative of the global population (Malik et al, 2021;Ostermann, 2021) or even of all Twitter users (Sloan and Morgan, 2015). In order for posts to be geotagged with a precise location, users must override Twitter's default settings and specifically agree to the use of their precise location.…”

Section: Discussionmentioning

confidence: 99%

Visualizing emoji usage in geo-social media across time, space, and topic

Levi,

Hauthal,

Mukherjee

et al. 2024

Front. Commun.

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Social media is ubiquitous in the modern world and its use is ever-increasing. Similarly, the use of emojis within social media posts continues to surge. Geo-social media produces massive amounts of spatial data that can provide insights into users' thoughts and reactions across time and space. This research used emojis as an alternative to text-based social media analysis in order to avoid the common obstacles of natural language processing such as spelling mistakes, grammatical errors, slang, and sarcasm. Because emojis offer a non-verbal means to express thoughts and emotions, they provide additional context in comparison to purely text-based analysis. This facilitates cross-language studies. In this study, the spatial and temporal usage of emojis were visualized in order to detect relevant topics of discussion within a Twitter dataset that is not thematically pre-filtered. The dataset consists of Twitter posts that were geotagged within Europe during the year 2020. This research leveraged cartographic visualization techniques to detect spatial-temporal changes in emoji usage and to investigate the correlation of emoji usage with significant topics. The spatial and temporal developments of these topics and their respective emojis were visualized as a series of choropleth maps and map matrices. This geovisualization technique allowed for individual emojis to be independently analyzed and for specific spatial or temporal trends to be further investigated. Emoji usage was found to be spatially and temporally heterogeneous, and trends in emoji usage were found to correlate with topics including the COVID-19 pandemic, several political movements, and leisure activities.

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“…[8] for an extensive list), but we would not expect significantly differing results. Practical reproductions of papers, and even more so replications of fundamental works, are promising projects to convincingly underpin a call for a culture change [29]. A successful reproducibility turn would not mean that every reproducible paper would be fully reproduced, nor would this be necessary.…”

Section: Discussionmentioning

confidence: 99%

Reproducible Research and GIScience: an evaluation using GIScience conference papers

Ostermann¹,

Nüst²,

Granell³

et al. 2020

Preprint

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GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are

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Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart Cities

Cited by 7 publications

References 65 publications

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Visualizing emoji usage in geo-social media across time, space, and topic

Reproducible Research and GIScience: an evaluation using GIScience conference papers

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