000			03601cam a22003977 4500
001			w24462
003			NBER
005			20211020104603.0
006			m o d
007			cr cnu||||||||
008			210910s2018 mau fo 000 0 eng d
100	1		_aAzzimonti, Marina. _928091
245	1	0	_aSocial Media Networks, Fake News, and Polarization / _cMarina Azzimonti, Marcos Fernandes.
260			_aCambridge, Mass. _bNational Bureau of Economic Research _c2018.
300			_a1 online resource: _billustrations (black and white);
490	1		_aNBER working paper series _vno. w24462
500			_aMarch 2018.
520	3		_aWe study how the structure of social media networks and the presence of fake news affects the degree of misinformation and polarization in a society. For that, we analyze a dynamic model of opinion exchange in which individuals have imperfect information about the true state of the world and exhibit bounded rationality. Key to the analysis is the presence of internet bots: agents in the network that spread fake news (e.g., a constant flow of biased information). We characterize how agents' opinions evolve over time and evaluate the determinants of long-run misinformation and polarization in the network. To that end, we construct a synthetic network calibrated to Twitter and simulate the information exchange process over a long horizon to quantify the bots' ability to spread fake news. A key insight is that significant misinformation and polarization arise in networks in which only 15% of agents believe fake news to be true, indicating that network externality effects are quantitatively important. Higher bot centrality typically increases polarization and lowers misinformation. When one bot is more influential than the other (asymmetric centrality), polarization is reduced but misinformation grows, as opinions become closer the more influential bot's preferred point. Finally, we show that threshold rules tend to reduce polarization and misinformation. This is because, as long as agents also have access to unbiased sources of information, threshold rules actually limit the influence of bots.
530			_aHardcopy version available to institutional subscribers
538			_aSystem requirements: Adobe [Acrobat] Reader required for PDF files.
538			_aMode of access: World Wide Web.
588	0		_aPrint version record
690		7	_aC45 - Neural Networks and Related Topics _2Journal of Economic Literature class.
690		7	_aC63 - Computational Techniques • Simulation Modeling _2Journal of Economic Literature class.
690		7	_aD72 - Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior _2Journal of Economic Literature class.
690		7	_aD8 - Information, Knowledge, and Uncertainty _2Journal of Economic Literature class.
690		7	_aD83 - Search • Learning • Information and Knowledge • Communication • Belief • Unawareness _2Journal of Economic Literature class.
690		7	_aD85 - Network Formation and Analysis: Theory _2Journal of Economic Literature class.
690		7	_aD91 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making _2Journal of Economic Literature class.
700	1		_aFernandes, Marcos.
710	2		_aNational Bureau of Economic Research.
830		0	_aWorking Paper Series (National Bureau of Economic Research) _vno. w24462.
856	4	0	_uhttps://www.nber.org/papers/w24462
856			_yAcceso en línea al DOI _uhttp://dx.doi.org/10.3386/w24462
942			_2ddc _cW-PAPER
999			_c323660 _d282222