Lack of Deep Information in Football News Analysis: When an 'Empty Structure' Cannot Replace Journalism

A recent output from an automated news analysis system has raised a serious question in the field of sports journalism and AI-driven content production. The system's first stage (Stage-1) is producing an output where all structural fields are present — title, source, summary, author stance, information points, entities involved, and time sensitivity — but all values are empty. The only available information is 'domain_label: football'. This means the analysis system has no actual content to work with. This situation is itself a major technical and editorial failure. For a journalist or analyst, a structure with empty fields is useless. If analysis were attempted on this basis, the only path would be imagination and speculation, which goes against the fundamental principles of professional journalism. That is why the analysis system explicitly stated that deep analysis is currently impossible. This honesty is also an important signal — AI systems should learn to admit 'not knowing' rather than fabricating stories to fill the gaps. To understand the root of the problem, it is necessary to examine why Stage-1's output is empty. According to experts, this type of output — where all fields exist but values are empty — does not indicate 'thin article content' but rather a 'silent failure' of the extraction process. That is, the system did not give an error message but actually extracted nothing. There could be several possible causes. The first and most likely cause is that the upstream process did not pass the body text at all. Web scraping may have succeeded, but the correct text selector did not work, and only navigation or copyright sections were obtained. The extraction engine returned an empty list but did not report an error. The second cause could be language or encoding — if the original text is in a language other than English and the extraction templates are designed only for English, the text cannot be parsed. The third cause could be truncation — if the input exceeds the context window, only the title area may remain. The fourth cause could be excessive strictness in prompt engineering — an instruction to 'output only strict JSON' without examples (few-shot) may have led the model to choose an empty structure rather than an error. The fifth and least likely cause is that the article was genuinely very short, such as a one-line news brief or image caption. The greatest danger of this failure is that it leads to 'incorrect analysis'. The time sensitivity of football content is extremely high — before a match, after a match, and during the transfer window, the meaning of the same fact changes completely. If time sensitivity has not even been assessed, the direction of the analysis itself can be wrong. Furthermore, the word 'football' itself is ambiguous — is it soccer or American football? Based solely on this tag, it is impossible to determine the terminology and competition context. Therefore, the most appropriate step in the current situation is to first find out why Stage-1 is empty. Experts suggest first examining the length and character distribution of Stage-1's original input, then checking whether the extraction engine has a fallback for 'empty results'. The correct approach should be to explicitly declare failure on an empty result rather than returning an empty shell. There are three options to continue the analysis. First, provide the full text of the original article — this is the best option, as Stage-1 can then be skipped and analysis conducted directly. Second, re-run Stage-1, this time asking it to output the number of information points and the word count of the original text as self-check indicators. Third, manually provide a few information points — who, when, what happened, what numbers — and analyze based on these, while clearly marking the boundaries of inference. This incident also points to the broader limitations of AI-driven news production. Automated systems are good at speed and scale, but when inputs are insufficient, they often fall back on 'empty structures', which can confuse readers and damage journalistic credibility. A responsible system should learn that when it does not have enough information, it should clearly say 'I don't know' rather than presenting speculative analysis to fill the gaps. In sports journalism, where facts and temporal context are paramount, this principle becomes even more important. Finally, this case is a warning for technical teams: 'silent failures' in extraction systems must be eliminated and explicit error handling must be implemented. At the same time, editorial teams must ensure that no automated output is published without human review, especially when the structure is empty. Only in this way can AI tools become assistants to journalism, not its replacement.