Computational journalism involves the use of computers for journalistic purposes. This technology helps in information gathering, organization, and sense-making, as well as in the communication and dissemination of news information. However, computational journalism must also follow traditional values of journalism. Here, we will discuss a few key points related to computational journalism.
Investigative journalism
Investigative journalism using computational journalism combines the power of computers with a human-centred approach to storytelling. The growing digital media landscape makes it possible for journalists to collaborate across national and international borders. International networks are particularly helpful for journalists working in countries with weak investigative reporting structures. Team members from different countries can contribute unique local knowledge and context. Different investigative journalists can also bring different technical skills to bear on an issue.
In recent years, data journalism has spread rapidly, particularly in investigative journalism. These journalists are interested in data that is classified or concealed, such as those provided by whistleblowers. Although this has advantages, using classified data can compromise journalistic independence. Woodall calls this phenomenon media capture. The increasing availability of data can overshadow investment in investigative journalism.
The MA in Data Journalism includes a course in computational journalism. This course equips students with the skills they need to start their career in data journalism. Students learn how to use database technology to create meaningful stories. They will also become familiar with data analysis techniques, including SQL, R, and JavaScript. They will also be introduced to statistical literacy and machine learning.
The use of these tools in investigative journalism has many advantages. For example, these tools are more efficient than traditional methods, allowing journalists to spend more time on the story and less time on research. While journalists are aware that their sources have agendas, it is not always easy to trace their motives. The tools can also assist in uncovering secrets that are otherwise hidden from the public.
In the 19th century, Florence Nightingale used graphics to campaign for better health services. Her “coxcomb” graphs showed the number of soldiers killed in a month, revealing that the vast majority of deaths were the result of bullets and preventable diseases. While computational journalism is not the same as investigative journalism, it has many of the same goals.
Investigative journalists can use these tools to explore hidden information from public records. The OCCRP’s Aleph investigative data platform, for example, enables journalists to follow the money. Aleph searches data from previous investigations, scraped databases, and official sources. It also allows researchers to upload their own files and summarize investigative findings. In addition, Aleph provides cross-references between people of interest.
Network analysis
Network analysis is an emerging area of computational journalism, which allows journalists to map online content, revealing patterns not visible through conventional computation. Journalists can use network analysis to better understand how news is disseminated and which online connections influence political opinions. As a result, the practice of network analysis is rapidly gaining popularity among journalists. But data-driven journalists face several challenges. First, the data may not be available to them at a single point in time. Secondly, journalists may not have access to all the information they require to write their stories.
Network analysis is useful in many different areas of journalism. For example, it can show the relationships between two entities, allowing journalists to identify leads and follow up on them. It can also be used to visualize a system so that readers can explore it more deeply. One example of how to use network analysis in news reporting is in the field of finance. The BBC has used the technique to map the relationships between two Twitter accounts and show how they influence each other.
One of the most commonly used tools for network analysis is Gephi, but it can be difficult to use and is not visually attractive. A more simple, yet still powerful, network analysis tool is NodeXL, a free open-source tool for Excel. Graph databases are also becoming a useful tool for journalists.
Network analysis is also useful in natural language processing and time series analysis. It can be used to define a group of users on a social network or to find a target group for advertising. A number of researchers are developing algorithms to solve this problem. These include the Kernighan-Lin algorithms and Spectral Clustering.
Currently, network analysis is largely left to researchers and scientists. However, it is essential for journalists to understand the flow of information on the internet and the sources of information. This knowledge can help them better understand how the media ecosystem functions.
Automated reporting
Automated reporting in journalism is the practice of using computers to produce news articles. It can improve the quality of journalism and save journalists time by freeing them up from routine tasks. This can free up more time for in-depth analysis, commentary, and investigative work. Journalists often spend long hours on research and analyzing content, but automated reporting saves them countless hours.
The use of automated journalism in journalism isn’t limited to newspapers or magazines. It has also been used in the field of data analysis. For example, a machine that searches for storylines in newly published datasets would be able to identify relevant stories. It would then be able to author a template using a third-party software tool. Then, journalists could decide whether or not to publish the data in the news or to tweak it.
However, the use of automation in journalism raises some concerns. For one, the sheer volume of news content generated will increase. News consumers will also experience a greater burden when trying to find relevant content. Furthermore, the increasing personalized nature of news may lead to a fragmentation of public opinion. The use of algorithms in journalism may also displace journalists’ traditional role as watchdogs of the government.
Another concern is the quality of news output. Currently, automated news does not produce news of comparable quality as human-written news. However, the quality of automated news is expected to improve as technology improves. It’s important for journalists to continue their education and develop skills that algorithms cannot perform. As the use of algorithms continues to grow, journalism will likely face major changes.
Automated journalism is a disruptive innovation, but it is one that’s likely to stay. The main drivers for this phenomenon include the availability of structured data, the increasing volume of news, and the desire to reduce costs. This one-hour webinar explores the current state of automated journalism and the issues it raises. It also outlines potential implications and avenues for further research.
Automated reporting in journalism may have a beneficial impact in places where local media is weak or nonexistent. In this case, AI services could be especially helpful.
Data visualization
The use of data visualization in computational journalism is a relatively new field of study. It has gained popularity among journalists, designers, and programmers due to its potential to effectively express complex information. However, despite its popularity, few studies have specifically investigated the role of data visualization in journalism. The use of data visualization can improve storytelling, particularly for news and features that involve readers.
Many people in the computational journalism community are eager to learn new tools and techniques. These practitioners include journalists, students, and experienced practitioners. The community has a culture of innovation, and tools are evolving rapidly. While some tools will fall out of vogue, others are constantly being created by passionate individuals. Often, tools are upgraded, building upon previous versions, and others are completely new.
As data and the news environment continue to become more complex, journalists will need to develop new ways to create, visualize, and present their stories. Data visualization has the potential to become the norm for news and features. In addition to news production, it can also be used for analysis and presentation. For example, journalists may use data visualization to understand how data sets are arranged.
Data visualization has a long history in the journalism field, and is a growing trend. In this course, students will examine various types of charts and evaluate their pros and cons. They will also learn to recognize the different types of graphical forms and how to determine which one is best for a particular situation. They will also read a classic book by Edward Tufte, which outlines the history and current uses of data visualization in journalism.
In addition to making data easy to understand, data visualization can also enhance the journalist’s duty to understand a subject. Data visualization can help journalists establish facts and provide broader contexts for their news stories. It can also support the creation of new contents that can be shared with stakeholders. For example, one participant noted that data visualization can aid the press office in creating the context needed for press coverage.
In a recent study of computational journalism, a team of Microsoft engineers created a platform to help journalists visualize data and tell stories using interactive data. The company developed the platform Flourish, a platform for interactive storytelling and data visualization. The development of Flourish was the result of a collaboration between two award-winning data journalists. The company is committed to creating tools for journalists to make their work more engaging and powerful.
