As the use of social platforms continues to evolve, in areas such as cyber-security and defence, it has become imperative to develop adaptive methods for tracking, identifying and investigating cyber-related activities on these platforms. This paper introduces a new approach for detecting cyber-related discussions in online social platforms using a candidate set of terms that are representative of the cyber domain. The objective of this paper is to create a cyber lexicon with cyber-related terms that is applicable to the automatic detection of cyber activities across various online platforms. The method presented in this paper applies natural language processing techniques to representative data from multiple social platform types such as Reddit, Stack overflow, twitter and cyberwar news to extract candidate terms for a generic cyber lexicon. In selecting the candidate terms, we introduce the APMIS Aggregated Pointwise Mutual Information Score in comparison with the Term Frequency-Term Degree Ratio (FDR Score) and Term Frequency-Inverse Document Frequency Score (TF-IDF Score). These scoring mechanisms are robust to account for term frequency, term relevance and mutual dependence between terms. Finally, we evaluate the performance of the cyber lexicon by measuring its precision of in classifying discussions as 'Cyber-Related' or 'Non-Cyber-Related'.