Mappa delle fonti per capitolo
Questa mappa collega i principali passaggi empirici del testo alle fonti riportate nella bibliografia. Non assegna una fonte a ogni frase: i passaggi dichiaratamente editoriali, le metafore e gli esempi costruiti non richiedono una fonte esterna. Serve invece a rendere trasparente quale letteratura sostiene i nuclei scientifici e metodologici del percorso.
Pariser (2011); Bakshy, Messing e Adamic (2015); Mansoury et al. (2020); Cinelli et al. (2021); Knudsen (2023); Khambatta et al. (2023); Eg, Demirkol Tønnesen e Kolberg Tennfjord (2023); Glickman e Sharot (2025).
Unkelbach et al. (2019); Corneille, Mierop e Unkelbach (2020); Hassan e Barber (2021); Prike ed Ecker (2023); Udry e Barber (2024); Wahlheim et al. (2025); Ye et al. (2026).
McCombs e Shaw (1972); Barberá et al. (2019).
Tversky e Kahneman (1981); Entman (1993); Kühberger (1998); Chong e Druckman (2007); Lecheler e de Vreese (2016).
Il capitolo svolge soprattutto una funzione analitica e applicativa; riprende la distinzione fra osservazione, interpretazione, framing e inferenza costruita nei capitoli precedenti.
Gigerenzer ed Edwards (2003); Reyna e Brainerd (2008); Campbell (2009); Hernán e Robins (2020).
Lord, Ross e Lepper (1979); Kunda (1990); Nickerson (1998); Hart et al. (2009); Garrett (2009a, 2009b).
Lord, Lepper e Preston (1984), insieme alle fonti su bias di conferma, esposizione selettiva e motivated reasoning richiamate nel capitolo precedente.
Il capitolo sintetizza soprattutto i concetti di indipendenza delle fonti, correggibilità e retroazione già documentati nei capitoli precedenti; per i feedback loop dei sistemi di raccomandazione: Mansoury et al. (2020).
Wineburg e McGrew (2019) per la lettura laterale; gli altri passaggi sono formulati come criteri generali di valutazione e non come risultati sperimentali specifici.
Wineburg e McGrew (2019) per la lettura laterale; Chesney e Citron (2019) per deepfake e liar’s dividend; C2PA (2026) per provenienza tecnica, Content Credentials e trasparenza sull’uso dell’IA. Il resto del capitolo applica i criteri di indipendenza delle fonti e framing già documentati.
Kahan et al. (2007), con la limitazione esplicitata nel testo: identity-protective cognition non viene utilizzata come spiegazione universale di ogni disaccordo.
Pezzuti, Leonhardt e Warren (2021); Brady et al. (2021); Burton, Cruz e Hahn (2021); Brady et al. (2023).
Vosoughi, Roy e Aral (2018); Walter e Tukachinsky (2020); Brashier et al. (2021); Juul e Ugander (2021); Pennycook et al. (2021); Pennycook e Rand (2022); Prike ed Ecker (2023); Wahlheim et al. (2025).
Rosenthal (1979); Infante-Rivard e Cusson (2018).
Goodhart (1975); Muchnik, Aral e Taylor (2013); Kim (2018); Mansoury et al. (2020); Stsiampkouskaya et al. (2021).
Esempio fittizio e dichiaratamente didattico. Non introduce nuova evidenza empirica: applica congiuntamente gli strumenti documentati nei capitoli precedenti.
Bibliografia e fonti
La bibliografia raccoglie gli studi, le opere e i documenti tecnici esplicitamente richiamati nel testo e le principali fonti utilizzate per sostenere i passaggi empirici. Le categorie editoriali e le metafore introdotte nel percorso non vengono presentate come concetti appartenenti alla letteratura scientifica. Per gli articoli è indicato, quando disponibile, il DOI; per gli standard tecnici viene collegata la specifica di riferimento. I riferimenti 2025-2026 sono stati ricontrollati sulle pagine editoriali disponibili: quando l’editore non espone ancora volume e pagine definitivi, resta indicata la pubblicazione online.
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132. DOI
Barberá, P., Casas, A., Nagler, J., Egan, P. J., Bonneau, R., Jost, J. T., & Tucker, J. A. (2019). Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data. American Political Science Review, 113(4), 883-901. DOI
Brady, W. J., McLoughlin, K., Doan, T. N., & Crockett, M. J. (2021). How social learning amplifies moral outrage expression in online social networks. Science Advances, 7(33), eabe5641. DOI
Brady, W. J., McLoughlin, K. L., Torres, M. P., Luo, K. F., Gendron, M., & Crockett, M. J. (2023). Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility. Nature Human Behaviour, 7, 917-927. DOI
Brashier, N. M., Pennycook, G., Berinsky, A. J., & Rand, D. G. (2021). Timing matters when correcting fake news. Proceedings of the National Academy of Sciences, 118(5), e2020043118. DOI
Burton, J. W., Cruz, N., & Hahn, U. (2021). Reconsidering evidence of moral contagion in online social networks. Nature Human Behaviour, 5, 1629-1635. DOI
Campbell, M. J. (2009). What about absolute risks? BMJ, 338, b1197. DOI
Chesney, R., & Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753-1820. DOI
Chong, D., & Druckman, J. N. (2007). Framing theory. Annual Review of Political Science, 10, 103-126. DOI
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. DOI
Coalition for Content Provenance and Authenticity (C2PA). (2026). Content Credentials: C2PA Technical Specification, version 2.4 (April 2026). SPECIFICA
Corneille, O., Mierop, A., & Unkelbach, C. (2020). Repetition increases both the perceived truth and fakeness of information: An ecological account. Cognition, 205, 104470. DOI
Eg, R., Demirkol Tønnesen, Ö., & Kolberg Tennfjord, M. (2023). A scoping review of personalized user experiences on social media: The interplay between algorithms and human factors. Computers in Human Behavior Reports, 9, 100253. DOI
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51-58. DOI
Garrett, R. K. (2009a). Echo chambers online? Politically motivated selective exposure among Internet news users. Journal of Computer-Mediated Communication, 14(2), 265-285. DOI
Garrett, R. K. (2009b). Politically motivated reinforcement seeking: Reframing the selective exposure debate. Journal of Communication, 59(4), 676-699. DOI
Gigerenzer, G., & Edwards, A. (2003). Simple tools for understanding risks: From innumeracy to insight. BMJ, 327(7417), 741-744. DOI
Glickman, M., & Sharot, T. (2025). How human-AI feedback loops alter human perceptual, emotional and social judgements. Nature Human Behaviour, 9, 345-359. DOI
Goodhart, C. A. E. (1975). Problems of monetary management: The U.K. experience. In Papers in Monetary Economics, Vol. I. Reserve Bank of Australia, Sydney.
Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135(4), 555-588. DOI
Hassan, A., & Barber, S. J. (2021). The effects of repetition frequency on the illusory truth effect. Cognitive Research: Principles and Implications, 6, 38. DOI
Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
Infante-Rivard, C., & Cusson, A. (2018). Reflection on modern methods: Selection bias – a review of recent developments. International Journal of Epidemiology, 47(5), 1714-1722. DOI
Juul, J. L., & Ugander, J. (2021). Comparing information diffusion mechanisms by matching on cascade size. Proceedings of the National Academy of Sciences, 118(46), e2100786118. DOI
Kahan, D. M., Braman, D., Gastil, J., Slovic, P., & Mertz, C. K. (2007). Culture and identity-protective cognition: Explaining the white-male effect in risk perception. Journal of Empirical Legal Studies, 4(3), 465-505. DOI
Khambatta, P., Mariadassou, S., Morris, J., & Wheeler, S. C. (2023). Tailoring recommendation algorithms to ideal preferences makes users better off. Scientific Reports, 13, 9325. DOI
Kim, J. W. (2018). They liked and shared: Effects of social media virality metrics on perceptions of message influence and behavioral intentions. Computers in Human Behavior, 84, 153-161. DOI
Knudsen, E. (2023). Modeling news recommender systems’ conditional effects on selective exposure: Evidence from two online experiments. Journal of Communication, 73(2), 138-149. DOI
Kühberger, A. (1998). The influence of framing on risky decisions: A meta-analysis. Organizational Behavior and Human Decision Processes, 75(1), 23-55. DOI
Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480-498. DOI
Lecheler, S., & de Vreese, C. H. (2016). How long do news framing effects last? A systematic review of longitudinal studies. Communication Yearbook, 40, 3-30. DOI
Lord, C. G., Lepper, M. R., & Preston, E. (1984). Considering the opposite: A corrective strategy for social judgment. Journal of Personality and Social Psychology, 47(6), 1231-1243. DOI
Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37(11), 2098-2109. DOI
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., & Burke, R. (2020). Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2145-2148. DOI
McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176-187. DOI
Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647-651. DOI
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220. DOI
Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Books.
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592, 590-595. DOI
Pennycook, G., & Rand, D. G. (2022). Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation. Nature Communications, 13, 2333. DOI
Pezzuti, T., Leonhardt, J. M., & Warren, C. (2021). Certainty in language increases consumer engagement on social media. Journal of Interactive Marketing, 53, 32-46. DOI
Prike, T., & Ecker, U. K. H. (2023). Effective correction of misinformation. Current Opinion in Psychology, 54, 101712. DOI
Reyna, V. F., & Brainerd, C. J. (2008). Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learning and Individual Differences, 18(1), 89-107. DOI
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638-641. DOI
Stsiampkouskaya, K., Joinson, A., Piwek, L., & Ahlbom, C.-P. (2021). Emotional responses to likes and comments regulate posting frequency and content change behaviour on social media: An experimental study and mediation model. Computers in Human Behavior, 124, 106940. DOI
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458. DOI
Udry, J., & Barber, S. J. (2024). The illusory truth effect: A review of how repetition increases belief in misinformation. Current Opinion in Psychology, 56, 101736. DOI
Unkelbach, C., Koch, A., Silva, R. R., & Garcia-Marques, T. (2019). Truth by repetition: Explanations and implications. Current Directions in Psychological Science, 28(3), 247-253. DOI
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. DOI
Wahlheim, C. N., Kemp, P. L., Wiley, R. W., & Engelhardt, A. M. (2025). Sustaining corrected beliefs in false news headlines over time: The roles of correction format and recognizing corrections. Memory & Cognition. Advance online publication. DOI
Walter, N., & Tukachinsky, R. (2020). A meta-analytic examination of the continued influence of misinformation in the face of correction: How powerful is it, why does it happen, and how to stop it? Communication Research, 47(2), 155-177. DOI
Wineburg, S., & McGrew, S. (2019). Lateral reading and the nature of expertise: Reading less and learning more when evaluating digital information. Teachers College Record, 121(11), 1-40. DOI
Ye, S., Attali, D., Ghazi, M., Cachia, A., Cassotti, M., & Borst, G. (2026). Systematic review and meta-analysis of the evidence for an illusory truth effect and its determinants. Nature Communications, 17, 3270. DOI