Computational Personality Prediction Based on Digital Footprint of A Social Media User

Deeva, Irina

doi:10.1016/j.procs.2019.08.194

Cited by 16 publications

(7 citation statements)

References 10 publications

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“…Once their primary models are stretched and adding data from other data sources, these are the same division. ese are possible to investigate learning [26]. Research has proposed a new framework for detecting online error and its localization using a scheme online for localizing and detecting errors in sensor data through recent processing tools of Big Data.…”

Section: Big Data Technologies and Their Managementmentioning

confidence: 99%

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

Chang

Nazir

2022

Scientific Programming

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Big Data is data whose shape and volume are rising with the passage of time and innovations in technology. This increase will give birth to more uncertain and complex situations, which will then be difficult to properly analyze and manage. Various devices are interconnected with each other, which communicate different types of information. This information is used for different purposes. A huge volume of data is produced, and the storage becomes larger. Computational modeling is the tool that helps analyze, process, and manage the data to extract useful information. The modern industry's challenge is to incorporate knowledge into Big Data applications to deal with distinguishing difficulties in computational models. The techniques and models are delivered with guides to help analysts quickly fit models to information insights. The decision support system is a strong system that plays a significant role in shaping Big Data for sustaining efficiency and performance. Decision-making through computational modeling is also a powerful mechanism for supporting efficient tools for managing Big Data for influential use. Keeping in view the issues of modern-day industry, the proposed study has been considered to present decision-making and computational modeling of Big Data for sustaining influential usage. The existing state-of-the-art literature is presented in an organized way to analyze the currently available research.

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Section: Big Data Technologies and Their Managementmentioning

confidence: 99%

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

Chang

Nazir

2022

Scientific Programming

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“…На пример, локације онлајн корисника се прате коришћењем уређаја које они носе са собом. Уређаји као што су мобилни телефони, дигитални сатови, напредни аутомобили и паметне куће сакупљају податке, што се другачије назива дигиталним отиском Deeva, 2019;Hinds & Joinson, 2019). Ови подаци о сваком онлајн кориснику укључују њихове навике и обрасце понашања, чак и временске податке, локације, здравствене информације и слично.…”

Section: алгоритамски утицајunclassified

“…Their movements and locations are tracked with the use of personal devices that online users carry physically with them. Gadgets such as mobile phones, digital watches, cars and smart houses gather data, which is otherwise called digital footprint Deeva, 2019;Hinds & Joinson, 2019). These big data about each one of online users include their habits and various patterns of behaviour, even weather data, locations, health patterns and so on.…”

Section: Algorithmic Influencementioning

confidence: 99%

Recommender algorithms as a source of power in contemporary society

Bojić¹

2022

Sociološki pregled

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Both tech companies and AI algorithms exercise immense power in today's globally interconnected world, which is based on big data and digital footprints of online users. This paper analyses the transfer of power from societies to tech companies and algorithms with the aim of examining whether recommender algorithms can be considered a public good. Deployed methods include content analysis and literature reviews. The study has found that control exercised over public opinion, decisions and moods of online users is unprecedented to such a high degree in human history. The above-mentioned control is based on the impact of both tech companies and algorithms. The limitation of this research is the lack of quantitative analysis. Future research should concentrate on defining recommender algorithms as a public good and analyzing how different media content, including virtual reality, affects citizens' psychology.

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“…Around 4.6 billion users are currently active on social networks worldwide ( Dixon, 2022 ). In the context of classic mass media such as radio, television, and newspapers, these users are not merely passive recipients but create an active and passive digital footprint ( Deeva, 2019 ). Given the fact that in terms of the world’s total population (7.9 billion people), social networks are used by around 58% of the population ( Worldometer, 2022 ), analysis of this big data is important in understanding the attitudes, experiences, and behaviours of the individual users on these platforms ( Childers, Lemon & Hoy, 2019 ; Pilar et al, 2017 ; de Veirman, Cauberghe & Hudders, 2017 ; Zhang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Global analysis of Twitter communication in corporate social responsibility area: sustainability, climate change, and waste management

Kvasničková Stanislavská,

Pilař,

Vogli

et al. 2023

PeerJ Computer Science

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Many people now consider social media to be an integral part of their daily routines, which has enabled companies to implement successful corporate social responsibility campaigns through these platforms. The direct interaction with stakeholders offered by social media helps companies to build understanding, trust, and their image. The aim of this study was to identify key topics and trends communicated in connection with corporate social responsibility on the Twitter social network from 2017 to 2022. Analysis of 520,638 tweets by 168,134 unique users identified a predominance of environment-related topics: Sustainability, Climate Change, and Waste management. However, Charity remains the largest single topic. Based on the trend analysis, the areas of ESG, Social Impact, and Charity were identified as growth areas in communication, while Green and Philanthropy, on the other hand, were identified as decreasing.

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Computational Personality Prediction Based on Digital Footprint of A Social Media User

Cited by 16 publications

References 10 publications

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

Recommender algorithms as a source of power in contemporary society

Global analysis of Twitter communication in corporate social responsibility area: sustainability, climate change, and waste management

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