![]() Looking at the map of which countries are searching the most for the Topic United Nations, Latvia dominates the list, while the remaining countries are almost exclusively French and Spanish-speaking nations. What could be driving this? The primary factor appears to be the inclusion of searches incorporating the word “un” as a proxy for the United Nations. However, looking at worldwide searches for the Topic in the timeline below, it is almost a mirror image of the one above, exhibiting a linear increase globally in interest in the United Nations. Searches in the United States for the Topic “United Nations” show relatively stable search interest, driven primarily by the Topic’s inclusion of the acronym “un” that is commonly used as shorthand to refer to the agency. To demonstrate this, the timeline below shows searches within the United States for the English phrase “united nations.” Immediately clear is the steadily declining interest in the organization over the last 10 years, which is also seen in worldwide searches for the phrase.Īn Arabic or Japanese speaker, however, would likely not use the English phrase “united nations” and thus Google helpfully has the Topic “United Nations” that groups together its common spellings and names in other languages. On the other hand, linguistic overlap, in which a word exists in multiple languages with very different meanings in each language, can complicate the ability to use Topics for multilingual searches. Topics can therefore be extremely powerful, grouping together translations into many languages under a single heading. Google gives the example of the Topic “Tokyo - Capital of Japan” incorporating terms like 東京, Токио, Tokyyo, Tokkyo and related phrases like “Japan Capital.” Examining search interest in the Topic “Pizza” instead of the exact English keyword “pizza” yields an identical interest timeline, but a very different geographic map – this time with interest centered in Italy and Europe rather than the United States (though the United States is still strongly represented). To assist with understanding topics in a multilingual world, Google Trends offers “Topics” in which predefined thematic headings group all related words, alternative spellings, and names in other languages under a single label. ![]() To accurately understand the global geography of search interest for pizza, one must search for its name across all the world’s languages. The reason for this is that obviously “pizza” is an English word and so the resulting map reflects only English-language web users. Looking at the map below, it would appear that the United States, Canada, and Australia/New Zealand are all the leading countries searching for pizza, with its Italian birthplace ranking relatively low. It is obvious from the search volume timeline that the web-searching public has grown steadily more interested in this menu item over the last decade at an almost perfectly linear rate. Everything from “pizza near me” to coupons to recipes can all be found in the list of searches. You can see a glimpse of this complexity by glancing at the Google Trends entry for “pizza” and scrolling to the bottom of the page to see the list of related searches. Each of these implies a very different definition of relevance in terms of what kinds of pages should be returned to the user. Let's begin by imagining a one-word Google search for “pizza.” To return the most relevant results, Google must determine from this single word whether the user would likely want a list of nearby pizza restaurants (perhaps someone new to the area), a list of today’s lunch specials (someone who already knows the nearby restaurants and is deciding where to go for lunch), a list of pizza recipes (someone wanting to cook it themselves), a history of pizza and its global impact (a student writing a school paper), or perhaps the latest pizza trends (a chef experimenting with new ideas). Here I explore how Google Trends attempts to address the multilingual challenge and also the limits of their approach and where it can yield contradictory results. In particular, attempts to use search data to understand macro-level societal patterns quickly run into challenges stemming from the fact that searches for the same topic may involve very different words across the world's languages. ![]() The increasingly multilingual nature of the modern web and the rich linguistic variety that comes with it makes this an ever more complicated process. ![]() ![]() At the core of all search engines like Google is the need to properly interpret highly ambiguous questions that may be just a few words long, anticipate the user’s intent in asking the question, and determine the most relevant information to return based on how the user is most likely to use it. ![]()
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