Sampling and bias aside the data may be generated with malicious intent, such as deception. Deception is a complicated (broad, situational, vague) concept. It seems improbable that an automated computer system would be able to find deception as such. Instead, we argue that the role of a system would be to aid the human analyst by detecting indicators, or clues, of (potential) deception. Indicators could take many forms and are typically neither necessary nor sufficient for there to be an actual deception. However, by using one or combining several of them a human may reach conclusions. Indicators are not necessarily dependent and will be added to or removed from the analysis depending on the circumstances. This modularity can help in counteracting/alleviating attacks on the system by an adversary. If we become aware that an indicator is compromised we can remove it from the analysis and/or replace it with a more sophisticated method that give us a similar indication.
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Ulrik Franke, Magnus Rosell, "Social media for intelligence: research, concepts, and results," Proc. SPIE 9851, Next-Generation Analyst IV, 98510H (12 May 2016);