Social media has become an essential part of our daily lives, and it has revolutionized the way we communicate and share information. However, it has also become a new topic in digital forensics, as social media evidence can provide significant support for investigating various offenses. Exploring social media information to give the government potential proof of a crime is not an easy task. It requires advanced digital forensic techniques, such as natural language processing (NLP) and blockchain, to extract and analyze the data.
Digital forensic investigation is the process of collecting, analyzing, and preserving electronic evidence from digital devices and networks to support investigations and legal proceedings. Social media platforms, such as Facebook, Twitter, and Instagram, have become a goldmine of potential evidence for digital forensic investigators. Posts, messages, photos, and videos shared on social media can provide valuable insights into a suspect’s behavior, intentions, and connections.
NLP techniques have been widely used in digital forensic investigations to extract and analyze social media data. The main reason for using NLP in this process is for data collection analysis, representations of every phase, vectorization phase, feature selection, and classifier evaluation. NLP helps in understanding and processing human language, which is crucial for analyzing social media data. NLP techniques can identify patterns, sentiment, and context in social media posts, which can provide important clues to investigators.
Applying a blockchain technique in this system secures the data information to avoid hacking and any network attack. Blockchain technology can provide secure and tamper-proof storage and sharing of data. In the context of digital forensics, blockchain can help ensure the integrity and authenticity of digital evidence. The blockchain framework proposed in this process provides a secure and decentralized platform for storing and sharing digital evidence.
The system’s potential is demonstrated by using a real-world dataset. The dataset consists of tweets related to the 2016 US Presidential Election. The NLP techniques were used to extract features such as sentiment, emotion, and topic from the tweets. The extracted features were then fed into a machine learning algorithm to classify the tweets into four categories: positive, negative, neutral, and mixed. The results show that the proposed system can accurately classify the tweets, which can provide valuable insights into the public sentiment towards the election.
Wrapping it up
Social media evidence is becoming a crucial source of evidence in digital forensic investigations. The use of advanced techniques, such as NLP and blockchain, can help extract, analyze, and preserve social media data in a secure and tamper-proof manner. The system’s potential is demonstrated by using a real-world dataset, which shows that the proposed system can accurately classify social media posts. As social media continues to grow and evolve, digital forensic investigators must keep up with the latest techniques and technologies to effectively extract and analyze social media data.
