  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'.
