چکيده
Social media platforms like Facebook, Twitter, and Instagram have transformed
communication but also amplified the spread of misinformation, leading to significant damage to
businesses, governments, and individuals. Traditional fact-checking methods struggle to keep up
with the speed and volume of content online, but machine learning and deep learning techniques,
such as CNNs and LSTMs, offer hope in detecting misinformation more effectively. However,
these methods face challenges in understanding the global dynamics of falsehoods' spread,
necessitating further research and innovation to combat this growing problem.
To improve rumor detection using deep learning, several key strategies are recommended. First,
it's crucial to develop models that capture both local and global context in information propagation,
incorporating user behavior analysis and network-based features. Second, integrating multimodal
data, such as text with images, videos, and user interactions, can enhance detection accuracy. Third,
employing ensemble methods that combine the strengths of various deep learning architectures
may improve robustness against evolving misinformation tactics. Lastly, continuous training and
updating of models with real-time data are essential to keeping up with the rapidly changing nature
of social media content.
The spread of misinformation is not a new problem, but it has become increasingly complex and
challenging to address in the digital age. Traditional methods of debunking rumors, such as manual
fact-checking, are insufficient against the rapid spread of misinformation online. Machine learning
and deep learning techniques offer promising solutions, but they need to account for the complex
dynamics of information spread on social media to be truly effective. Ongoing research and
innovation are required to develop tools that can detect and counteract misinformation before it
causes significant harm.
