Preserving human mental health through control of pathogenic text in mass media by means of indexing and marking
Subject of this research paper is problem of pathogenic text as method for manipulating human consciousness and its dissemination through mass media, which due to their specific, make such manipulation most effective. Mass media have mastered metaphoric language, which can flawlessly influence readers’ imagination.
We need to separately highlight our interest in text-based mass media (printed or blogs) versus audio-visual mass media (broadcast and digital), where flow of negative information seems to be magnitudes larger. In particular, paper touches specifics of written information perception.
Paper describes different negative consequences of pathogenic information consumption for human mental health, such as: lack of creative activity, depression, ambivalence, development of adrenaline addiction, etc.
In this paper, we analyze existing solutions of the problem of negative impact of pathogenic information, implemented in various countries and communities, substantiating their deficiencies in today’s realities, especially considering opposition to censorship and governmental limitations.
We see resolution for the pathogenic text influence on human consciousness in person herself, in her self-awareness and ability to independently assess situation and make decisions. One of approaches to protecting society from pathogenic text without censorship, could be marking of pathogenic level of each specific article or publication. We also suggest not to limit markings to “pathogenic” or “non-pathogenic” labeling, but show percentage of text pathogenicity. By informing consumer of level of negative impact by particular text, we give him/her opportunity to decide about necessity or desire to read this text.
We propose automatic classification method based on Bayesian filters (Himmelblau, 1970), (Yerazunis, 2003).
Keywords: mental health, informational warfare, mass media, information, consciousness, influence, protection, text classification.