A Loopy Belief Propagation Factor Graph Simulation of my Grandma Nonna’s Insane Facebook Feed

Dr. Zark Muckerberg1

1 Department of Social De-Engineering, Cranberry-Lemon University, Pittsburgh, PA, USA


The content emerging from my Grandma Nonna’s facebook feed has been becoming alarmingly more and more insane. Though she has historically been a loving grandmother, too apolitical, and polite to say anything controversial, her feed has been fast filling up with emotionally charged political dank memes completely uncharacteristic of historic Grandma Nonna data [1]. Worryingly, her extreme political rhetoric has oddly emerged from extreme right and left view points, socially isolating her from her weekly card and zoomba groups. My fridge is fast filling up with her pot roasts and potato salads left unconsumed, a clear indicator of drama at the church potluck [2]. By abusing a Loopy Belief Propagation (LBP) bidirectional factor graph, this paper successfully simulates and explains the Grandparent Radicalization Process (GRP). By miss applying The Sum-Product Algorithm, Nonna re-filters dank political memes passed recursively and acyclically using Meme Reverse Political Polarization (MRPP) [3] through a disguised Russian troll farm, Uncle Johnny’s far right political facebook groups, and Grand Aunt Elizah’s far left facebook groups.  

Keywords: Factor Graphs, Loopy Belief Propagation, Fake News, Trust Networks, Echo Chambers, Machine Learning

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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