You are choosing keywords for a paper WRONG! Do THIS instead...

Choosing the right keywords for a research paper isn’t a cosmetic step—it determines whether search engines can match the work to what scientists are actively typing. The biggest leverage comes from placing high-demand, field-relevant terms in the paper’s title and abstract, where search engines pull metadata used by Google Scholar and other indexes. Keywords typed into the “keyword” field during submission matter, but they’re often treated like an afterthought; the more reliable path to discoverability is aligning the paper’s most prominent text with the queries people already use.

A common mistake is selecting phrases that are technically accurate but too niche for anyone to search. Even researchers in the same specialty may not use the exact wording found in a paper’s methods or jargon. The practical goal is to aim for the “middle” of a search pyramid: broad enough to capture meaningful search volume, but specific enough to still point to the actual research contribution. Research lives at the top of the pyramid—too new or too obscure to be searched directly—so authors should instead target the terms that sit beneath it: the problems, concepts, and commonly searched subtopics that lead readers to the paper.

To find those terms, the transcript recommends a simple workflow built around how autocomplete and question prompts behave in search. Start with Google Scholar: type the field, then iterate through variations while watching what Google Scholar suggests in autocomplete. Because autocomplete is driven by frequent queries, it’s treated as a strong signal that real people search for those phrases. Keep a running list in a spreadsheet or document as the search terms evolve.

Next, repeat the exploration on regular Google, with the browser logged out to avoid personalized search history. Then use Answer the Public to map what people ask about a topic. If Answer the Public returns few or no results, that’s a sign the query is too far up the pyramid—go broader until you see enough traction. The transcript gives an example from solar-cell research, where “transparent semiconductor solar cells” was too narrow, while “organic photovoltaic solar cells” and “solar cells” provided better starting points.

Once a keyword list exists, effectiveness comes down to two criteria: search demand and how naturally the phrase fits into the title and abstract. Exact search-volume data is hard to obtain for academic queries because major tools are optimized for advertising keywords, not low-volume scientific terms. Still, autocomplete presence, Answer the Public results, and “People also ask” style prompts can serve as practical proxies. For additional estimates, Google Keyword Planner and tools like Ubersuggest can help, though they may be less reliable for science.

Finally, the transcript emphasizes a balancing act: high-volume terms must still read like normal academic English and pass a common-sense check for peer review. Keyword stuffing is discouraged; the phrase should integrate smoothly into sentences and reflect the actual research. Used consistently across papers, conference presentations, and any work with a title and abstract, this approach can make research easier to find—and easier to cite. It also suggests a secondary use: letting search demand guide future research directions by identifying which related questions attract more attention, then steering toward the most searchable branch of inquiry.