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 semiquantitative 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. The main findings are: (1) we introduce specific politicized social channels of note; (2) we present viable indicative AI/ML modes of characterizing the output of these channels for capturing and tracking mal-info; (3) we provide an approach for qualitative and semi-quantitative signatures of mal-info; (4) we flag the impending use of mal-GenAI in this context. We emphasize that mal-info in social media will be exploited by state sponsored malicious actors, and it is essential to initiate studies to capture, track, and mitigate the disinformation. We present a representative case study mode which, when expanded upon, can help mitigate the weaponization of social media intended to attack various subgroups or communities. This is a serious challenge for the times and needs urgent attention.

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