ArticleAn international journal on information fusion2016
Social big data: Recent achievements and new challenges.
Article in An international journal on information fusion, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 837 citations in OpenAlex.
- Construction of a Theoretical Framework for Scientific Data Governance.Scientific data · 2026Article
- Social media exposure, school prevention, and adolescent vaping: a structural equation model-based secondary analysis of the Florida youth tobacco survey.Frontiers in public health · 2026Article
- The psychological motivation of users actively constructing information cocoons from the perspective of Adler's teleology: an empirical study based on a sample of Chinese university students.Frontiers in psychology · 2026Article
- Analyzing perception of Over-The-Top (OTT) sports broadcasting focus on social media big data analytics.Heliyon · 2025Article
- Article
- A comprehensive review of security threats and solutions for the online social networks industry.PeerJ. Computer science · 2023Article
- Improving child health through Big Data and data science.Pediatric research · 2023Review
- Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine.Computational intelligence and neuroscience · 2023Article
- MLM-based typographical error correction of unstructured medical texts for named entity recognition.BMC bioinformatics · 2022Article
- How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?Information technology & management · 2022Article
- Article
- A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges.Journal of ambient intelligence and humanized computing · 2022Article
- The Effect of Big Data-Based Digital Payments on Household Healthcare Expenditure.Frontiers in public health · 2022Article
- Review
- Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World.Journal of healthcare engineering · 2022Article
- The dynamic evolution of collaborative networks in sustainable development: Untying impact of environmental policy in China using network-based text analysis approach.Frontiers in psychology · 2022Article
- Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion.BMC medical informatics and decision making · 2021Review
- Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology.Journal of integrative bioinformatics · 2021Review
- Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions.Neural computing & applications · 2021Article
- ReCognizing SUspect and PredictiNg ThE SpRead of Contagion Based on Mobile Phone LoCation DaTa (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence.Sustainable cities and society · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.