Use Cases for AI Tech in journalism
Recent advances in the area of dialog-based Large Language Models (ChatGPT, Bing Chat) have publicly demonstrated that generalized Natural Language Generation (NLG) models are capable of assisting content creators in many different tasks. This post intends to shed some light on the multitude of AI use cases along the journalistic production process.
From sentiment analysis and topic modeling to text generation and personalized content recommendations, AI can be harnessed to assist in every phase of the journalistic production process.
Research Phase
- Sentiment analysis can be used to gauge public opinion on a topic, helping journalists understand how their audience is likely to respond to news stories.
- Event detection can identify breaking news and emerging trends, enabling journalists to be the first to report on important stories.
- Predictive analytics can be used to forecast trends and predict the likelihood of future events, helping journalists stay ahead of the news cycle.
- Social Media Monitoring Tools that use AI to analyze social media for breaking news and trending topics, providing journalists with a rich source of real-time information.
- Automated Keyword research can help journalists identify topics with a high search volume and low competition, enabling them to produce more relevant content.
- Topic modeling can help journalists identify emerging trends, underreported topics, and mitigate newsroom biases for more inclusive and accurate reporting.
- Named entity recognition can be used to identify and track people, organizations, and locations in news articles, making it easier to follow stories as they develop.
- Language translation can assist journalists working in multilingual environments, enabling them to communicate with sources and report on global events more effectively.
- Text Summarization of long-form content can help journalists quickly identify key points and takeaways, enabling them to work more efficiently.
- Natural language generation (NLG) tools can help journalists to come up with fresh angles and new approaches to a story by generating article ideas for a topic in a specific format.
- AI-powered Fact-Checking, claim checking and image reverse search tools to verify information and flag potential inaccuracies, aiding journalists in assessing source credibility, including user-generated content.
- Deepfake Detection systems that can detect manipulated images or videos helping journalists to ensure that the content they use in their reporting is authentic and reliable.
- Automated Data Analysis can assist in analyzing large amounts of data, identifying patterns, and generating insights that can be used in news stories.
Production Phase
- Natural language generation (NLG) tools
- to automatically create content. The approach of using structured data such as financial reports or sports scores is being implemented by some news publishers.
- to assist with writing by suggesting changes or additions to a draft article
- to suggest article topics, headlines, snippets and keywords.
- Copy editing and proofreading assistance to catch spelling and grammar errors, and suggest rephrasing for clarity and readability.
- Speech-to-text transcription services that use speech recognition and natural language processing (NLP) to convert recorded interviews or press conferences into text.
- Automtaed video editing tools that use AI to analyze video footage and automatically generate a highlight reel or summary of key moments.
- Automated image analysis and tagging tools that use computer vision to identify and label the content of images used in news articles, such as identifying the people or objects depicted.
- Image generation (synthesis) AI to create realistic or abstract visualizations of hypothetical scenarios or complex concepts.
Distribution Phase
- A/B testing to optimize headlines, images, and other elements of content for maximum audience engagement
- Predictive analytics to determine the best times to post content on social media and other channels.
- Content personalization and recommendation engines that use AI to suggest relevant articles, videos, or podcasts based on a reader’s interests or browsing history.
- Sentiment analysis to gauge the emotional response of audiences to news stories and adjust content accordingly.
- Text classification and Keyword Extraction to automatically classify content into news categories, tags and journalistic formats.
- Audience segmentation and targeting based on user behavior and demographics to ensure that content is reaching the intended audience.
- Social media monitoring and analytics to track conversations and trends related to news stories and adapt content strategies accordingly.
- Ad targeting and optimization using AI to determine the most effective ad placements and content for specific audience segments.
- Moderation systems for user-generated-content (i.e. comments) to encourage constructive discussion and eliminate harassment and abuse.
TL;DR: AI will not replace the work of journalists, but can assist them in many of their daily tasks throughout the journalistic production process. In research, it can help with tasks such as social media monitoring and keyword research. In production, it can be used for natural language generation, transcription, editing and summarization. In distribution, it can help with content personalisation, audience targeting and moderation. The full list of use cases is even longer.
“Use cases for AI Tech in journalism” is the second part of our series News Snippet Generation. A Learning Journey on Open Source Large Language Models and how to assist journalists with generative AI in German.