Bringing Representativeness into Social Media Monitoring and Analysis

Kaschesky, Michael; Sobkowicz, Paweł; Lobato, José Miguel Hernández; Bouchard, Guillaume; Archambeau, Cédric; Scharioth, Nicolas; Manchin, Robert; Gschwend, Adrian; Riedl, Reinhard

doi:10.1109/hicss.2013.120

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…In public opinion mining, the problem of inadequate representativeness of results obtained from geo-social media can also be addressed by integrating results of a smaller opinion poll with known participant demographics and adjusting the results [29]. A similar approach suggests mitigating representativeness problems by measures such as qualitative analysis of a subset of the data in question or the integration of knowledge from the platform providers in the analysis process where possible [30].…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

Petutschnig

Resch

Lang

et al. 2021

IJGI

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Geo-social media data are widely used as a data source to model populations and processes in a variety of contexts. However, if the data do not adequately represent the population they are drawn from, analysis results will be biased. Unaddressed, these biases may lead to false interpretations and conclusions. In this paper, we propose a generic methodology for investigating the representativeness of geo-social media data for population groups of similar statistical predictive power based on reference data. The groups are designed to be spatially coherent regions with similar prediction errors. Based on these units, we investigate the influence of different socio-demographic covariates on the representativeness. We perform experiments based on over 1.6 billion tweets and 90 socio-demographic covariates. We demonstrate that Twitter data representativeness varies strongly over time and space. Our results show that densely populated areas tend to be underrepresented consistently in non-spatial models. Over time, some covariates like the number of people aged 20 years exhibit highly different effects on the prediction models, whereas others are much more stable. The spatial effects can most frequently be explained using spatial error models, indicating spatially related errors that indicate the necessity of additional covariates. Finally, we provide hints for interpreting the results of our approach for researchers using the concepts presented in this paper.

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Section: Related Workmentioning

confidence: 99%

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

Petutschnig

Resch

Lang

et al. 2021

IJGI

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“…Monitoring systems are used in many domains, e.g., environment monitoring [46], health monitoring [47], mechanical systems monitoring [48], information technology infrastructure monitoring [49], media monitoring [50], [51], business activity, and process monitoring [52]. Monitoring systems in the education domain are used mainly for education management solutions that ensure the monitoring of the execution of study plans [53]- [55].…”

Section: To Explore the Structural Characteristics Of Monitoring Systemsmentioning

confidence: 99%

Monitoring Services to Support Continuous Curriculum Engineering

Rudzajs

Kirikova

2021

CSIMQ

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Continuous and rapid developments in science and technology have raised a challenge for compliance between the learning content provided by study programs and (1) the actual state of art in the domains the programs are addressing and (2) the actual needs of companies that will employ the graduates of these programs. To achieve such compliance continuously, the digitalization of learning content (curriculum) engineering could provide supporting tools that facilitate awareness of incompliance, which is the first step in introducing changes in the learning content. In this article we discuss how such awareness, regarding the needs of companies, can be supported by a service system that monitors the gap between educational demand and offer. The proposed service system provides possibilities for automatic, semi-automatic and manual analysis of texts representing the educational demand, versus the texts representing the educational offer. The implementation of the service system has been demonstrated, having been applied at university. The experiments showed that the system can provide valuable information for educational content development, but its maintenance and incorporation in study process management and curriculum engineering still require additional research.

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“…The analysis of unstructured social media text has been used in a variety of contexts from government services [37], to marketing [21] and finance [15]. The domain of 'crisis informatics' provides a good example of how the noise in social media can be reduced to provide positive outcomes in near real-time For example, Palen et al [36] provide a framework by which people can judge the helpfulness of information by establishing source-level and individuallevel credibility.…”

Section: Background Literaturementioning

confidence: 99%

Understanding employee social media chatter with enterprise social pulse

Shami

Jiang

Panc

et al. 2014

Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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The rise of social media in the enterprise has enabled new ways for employees to speak up and communicate openly with colleagues. This rich textual data can potentially be mined to better understand the opinions and sentiment of employees for the benefit of the organization. In this paper, we introduce Enterprise Social Pulse (ESP) -a tool designed to support analysts whose job involves understanding employee chatter. ESP aggregates and analyzes data from internal and external social media sources while respecting employee privacy. It surfaces the data through a user interface that supports organic results and keyword search, data segmentation and filtering, and several analytics and visualization features. An evaluation of ESP was conducted with 19 Human Resources professionals. Results from a survey and interviews with participants revealed the value and willingness to use ESP, but also surfaced challenges around deploying an employee social media listening solution in an organization.

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Bringing Representativeness into Social Media Monitoring and Analysis

Abstract: Abstract

Cited by 11 publications

References 11 publications

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data

Monitoring Services to Support Continuous Curriculum Engineering

Understanding employee social media chatter with enterprise social pulse

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