How should the architecture of an effective computerised platform for detecting fakenews and other forms of disinformation on the internet built using Big Data Analytics, artificial intelligence and other Industry 4.0 technologies be designed?
The scale of the development of disinformation on the Internet including, among other things, fakenews has been growing in recent years mainly in social media. Disinformation is mainly developing on social media sites that are popular among young people, children and teenagers. The growing scale of disinformation is particularly socially damaging in view of the key objective of its pursuit by cybercriminals and certain organisations using, for example, the technique of publishing posts and banners using fake profiles of fictitious Internet users containing fakenews. The aim is to try to influence public opinion in society, to shape the general social awareness of citizens, to influence the assessment of the activities of specific policies of the government, national and/or international organisations, public or other institutions, to influence the ratings, credibility, reputation, recognition of specific institutions, companies, enterprises, their product and service offerings, individuals, etc., to influence the results of parliamentary, presidential and other elections, etc. In addition to this, the scale of cybercriminal activity and the improvement of cyber security techniques have also been growing in parallel on the Internet in recent years. Therefore, as part of improving techniques to reduce the scale of disinformation spread deliberately by specific national and/or international organisations, computerised platforms are being built to detect fake news and other forms of disinformation on the internet built using Big Data Analytics, artificial intelligence and other Industry 4.0 technologies. Since cybercriminals and organisations generating disinformation use new Industry 4.0 technologies in the creation of fake profiles on popular social networks, new information technologies, Industry 4.0, including but not limited to Big Data Analytics, artificial intelligence, deep learning, machine learning, etc., should also be used to reduce the scale of such harmful activities to citizens.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How should the architecture of an effective computerised platform for detecting factoids and other forms of disinformation on the Internet built using Big Data Analytics, artificial intelligence and other Industry 4.0 technologies be designed?
And what do you think about it?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
I think the use of big data analysis technique like spark, apache tools can detect fake news from online platform.
The architecture of an online fakenews detection platform built with Big Data Analytics and Artificial Intelligence should be designed to handle large volumes of data. The platform should use machine learning algorithms to analyze the data and detect patterns that indicate fake news. The platform should also be able to identify the source of the fake news and track its spread across social media platforms. The platform should be designed to be scalable and flexible so that it can adapt to changing data volumes and new sources of fake news.
A multi-faceted computerised platform for detecting fake news and other disinformation online, especially one that uses Big Data Analytics, AI, and other Industry 4.0 technologies, is needed. Here's a platform architectural outline and my thoughts on major components and strategies:
Components of architecture
1. Data Collection and Aggregation: - Collect data from internet sources, such as social media platforms, using web crawlers and APIs.
Use Big Data technologies like Hadoop or Spark to aggregate and store enormous amounts of data.
2. Data Preprocessing and Normalization: - Remove noise and normalise data format.
NLP can parse and interpret text.
3. Feature Extraction: - Use NLP to extract sentiment, subjectivity, writing style, and other linguistic traits.
Analyse metadata (source credibility, user profiles, network patterns).
4. Use AI and machine learning algorithms (e.g., SVM, Random Forest, neural networks) to categorise content as real or deceptive.
Transformers, BERT, and other deep learning methods can help you comprehend language context and nuances.
5. Real-Time Analysis: Apply a stream processing system for real-time data analysis.
Complex event processing engines can identify patterns and anomalies in data.
6. Verify and Fact-Check: - Use fact-checking APIs and databases to verify and cross-check information.
- Create a semi-automated system where specialists verify flagged content.
7. Feedback Mechanism: - Establish a feedback loop to enhance detection models based on current misinformation trends and techniques.
8. User Interface and Reporting: - Create an easy-to-use interface for monitoring and reporting.
Visualise trends and hazards with dashboards.
9. Security and Privacy: - Protect platform and user data with strong security measures.
- Follow ethics and privacy laws.
Personal Opinion:
To comprehend and counteract disinformation, computer science, journalism, psychology, and political science must be combined.
**AI Limitations** AI is powerful but not perfect. Overusing AI might cause biases and inaccuracies. Human monitoring is crucial.
Ethics: Disinformation detection must be balanced with free expression and privacy.
- Adaptable and evolving Disinformation methods change, therefore the platform must adapt.
In conclusion:
In conclusion, developing a disinformation detection tool in the digital age is difficult but essential. It demands combining modern technologies with human expertise and ethics. The fight against fake news and disinformation requires cross-disciplinary and sectoral coordination.
References for designing and developing a computerised platform to detect fake news and disinformation utilising Big Data Analytics, AI, and Industry 4.0 technologies:
1. "Big Data Analytics in Cybersecurity" by Onur Savas and Julia Deng. This book discusses big data analytics in cybersecurity, particularly disinformation detection.
2. "Deep Learning for Natural Language Processing: Creating Neural Networks with Python" by Palash Goyal and Sumit Pandey. Deep learning models are essential for false news identification, and this book covers their use in textual data processing and understanding.
3. Clarence Chio and David Freeman's "Machine Learning and Security: Protecting Systems with Data and Algorithms". This book discusses machine learning and security, providing ideas for disinformation detection.
4. "Social Media Data Mining and Analytics" by Gabor Szabo and Gungor Polatkan. Social media data mining is crucial to disinformation analysis and detection.
5. "Data-Driven Security: Analysis, Visualisation and Dashboards" by Jay Jacobs and Bob Rudis. Data security, including visualisation and analysis for a misinformation platform, is covered in this book.
6. "Cybersecurity – Attack and Defence Strategies: Infrastructure security with Red Team and Blue Team tactics" by Yuri Diogenes and Erdal Ozkaya. It provides cybersecurity strategies for disinformation detection platform development.
7. **"Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies" by Steven Finlay.** This guide explains how AI and ML in business can be used for cybersecurity and disinformation.
These references from academic databases or libraries provide a foundation in the technologies and methods needed to develop an effective Internet disinformation detection platform. Big data analytics, AI, cybersecurity, and social media analytics are covered.
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