Co-occurrence describes the common occurrence of two terms in a superordinate context. The word Co-Occurrence originates in Latin – it stands for occurar", which means "to appear" or "to appear". The addition "Co" aims at the fact that it concerns at least two terms, which stand in a close thematic connection.

General information[edit]

While Google has practiced a purely keyword-related search in the past, this has been replaced over time by the semantic method. This makes it easier to establish connections. If terms that are related to each other can be assigned thematically, the search results are more complete. For example, the use of synonyms allows Google to assign the terms "couch" and "sofa" to the same superordinate topic. Nevertheless, the semantic method reaches its limits, for example when it comes to homonyms. Unlike synonyms, these are not two different words that mean the same thing, but identical words with different meanings. For example, a band is both a musical group, and a ring. This is where Co-Occurrence comes in, because with the analysis of different terms, the generic term can be assigned more easily. If a text contains terms such as "music", or "guitar" in addition to the word band, it can be assumed that it is not a text describing the characteristics of a ring.

Effects of co-occurrence on search engine optimization[edit]

Co-occurrence also has an impact on SEO, because through this semantic method, many keywords can be included that did not previously play a role. This can be illustrated using the keyword "cat" as an example. Previously, Google only searched for the main keyword "cat". In the course of the semantic search synonyms such as "kitten" were added. Texts dealing with cats could thus be analysed more easily. Through Co-Occurrence further terms were added in the course of time, which were not directly related to cats. Thus, if the texts were aligned accordingly, under certain circumstances even terms such as "Batman" or "Spiderman" could also be thematically assigned to the cat.

The more terms were attached to the generic term cat, the more confusing the results could be. The interplay of terms such as "black cat" or "green eyes" in connection with thematically not direct keywords such as "Batman" or "Spiderman" could thus lead to search results that went in a completely different direction and led to "Hulk" or "Superman" via detours or the life of Co-Occurrence.

For search engine optimization, this methodology means that it is no longer sufficient to concentrate on the top 10 in order to attempt to rank better. Because in the course of the Co-Occurrence, Google has changed its own practice. In its attempt to analyze websites for keywords, Google has moved on to examine the first 50 places in order to be able to diversify as widely as possible. It can happen that websites that contain relatively many keywords that Google has classified as relevant to the topic push themselves in front of pages that were better before. Because if the pages listed in front do not contain terms that were also classified as relevant by Google in the course of the co-occurrence, this damages the ranking.

Differences between co-occurrence and co-citation[edit]

Co-occurrence and co-citation are often mentioned in one breath, sometimes they are also considered to be synonymous. But the approaches differ in detail. While Co-Occurrence, as described above, focuses on certain keywords in the analysis, co-citation focuses on websites. In co-citation, Google compares websites linked from a third party website (or more).

Importance for online marketing[edit]

The question, to what extent Co-Occurrence effects the SERPs (Search Engine Result Pages), cannot be answered conclusively. Experts’ opinions differ on this matter. However, one can assume that it cannot do any harm to use co-occurrence and co-citation, because the thematic proximity to expert sites and one's own high-quality content are generally rated positively.

Co-Occurrence and the SEO method TF*IDF are related. With the second method, selected keywords are set in relation to all texts that can be found on the web. Let me give you an example: If the word "Bratwurst" appears in a text dealing with the topic of grilling, this keyword ("Bratwurst") is given a higher status than in a text dealing with a completely different topic, but in which "Bratwurst" also appears, albeit much rarer. The TF*IDF method therefore takes into account the context and frequency of certain keywords. With TF*IDF, certain combinations of terms and thus the co-occurrence can be determined, both can then later be integrated into search engine optimization.