  This paper highlights the developing need for quantitative modes for
capturing and monitoring malicious communication in social media. There has
been a deliberate "weaponization" of messaging through the use of social
networks including by politically oriented entities both state sponsored and
privately run. The article identifies a use of AI/ML characterization of
generalized "mal-info," a broad term which includes deliberate malicious
narratives similar with hate speech, which adversely impact society. A key
point of the discussion is that this mal-info will dramatically increase in
volume, and it will become essential for sharable quantifying tools to provide
support for human expert intervention. Despite attempts to introduce moderation
on major platforms like Facebook and X/Twitter, there are now established
alternative social networks that offer completely unmoderated spaces. The paper
presents an introduction to these platforms and the initial results of a
qualitative and semi-quantitative analysis of characteristic mal-info posts.
The authors perform a rudimentary text mining function for a preliminary
characterization in order to evaluate the modes for better-automated
monitoring. The action examines several inflammatory terms using text analysis
and, importantly, discusses the use of generative algorithms by one political
agent in particular, providing some examples of the potential risks to society.
This latter is of grave concern, and monitoring tools must be established. This
paper presents a preliminary step to selecting relevant sources and to setting
a foundation for characterizing the mal-info, which must be monitored. The
AI/ML methods provide a means for semi-quantitative signature capture. The
impending use of "mal-GenAI" is presented.
