Narrative Spread in the Information Age: Adaptive Traits and Characteristics
The following article is an original work published by the Information Professionals Association. Opinions expressed by authors are their own, and do not necessarily reflect the views of or endorsement by the Information Professionals Association.
Humans use narratives to make sense of the world around them. By understanding narrative spread, we can make sense of how others are interpreting and interacting with the world. This is because people choose which narratives to attend to, consume, remember, and transmit in non-random ways. The purpose of this article is to outline the narrative traits and characteristics that increase the likelihood that it will be attended to, consumed, remembered, and transmitted. By understanding these traits, we can understand and interact with the information environment more effectively.
Social media platforms’ business model relies on ads to generate revenue. In fact, 97% of Facebook’s revenue and 87% of Alphabet’s revenue came from ads in 2017 (Ingram, 2017). This ad-reliant business model incentivizes platforms to increase user interaction by promoting high-engaging posts and news stories. Often, these new stories contain a narrative (description of a situation or event with added meaning or context) that captures users’ attention and promotes engagement. Understanding the micro and macro-level mechanisms that drive users’ attention and engagement is important for understanding how information, news stories, and narratives spread.
The modern socio-technical information environment enables unprecedented information to spread across the narrative landscape. Two defining characteristics of this narrative landscape are the overwhelming content volume and competing interpretations of local and global events. These characteristics mean an individual can only give attention to some narratives within the information environment. Because of this, not all narratives spread across the landscape. So, what narrative traits and environmental characteristics promote narrative spread?
Individual narratives embody “adaptive” traits that increase their likelihood of being attended to, remembered, adopted, and transmitted by people and algorithms. Content-based traits include those narratives that contain survival utility, literary devices, degrees of content familiarity, framing effects, and future utility. Cognitive-based traits include optimizing ordering effects, aligned gist representation, schema congruency, anchoring stimuli, and favorable exposure patterns. Finally, social effects include narratives that take advantage of providing social utility, eliciting social influence, aligning to social judgment, favorable transmitter characteristics, and social transmission filters. Successful narratives often take advantage of the interplay between these individual adaptive traits through uniquely viral trait combinations. Identifying narratives’ adaptive traits can help analysts, researchers, and decision-makers understand the spread of current narratives and develop their own narratives for the information environment.
First, we care about narratives that provide “survival utility” through fitness-related information, such as potential threats or environmental hazards. Researchers hypothesize this is because fitness-enhancing information molded our memory system during our hominin evolution (Nairne & Pandeirada, 2016). Others suggest that natural selection favored learning strategies that allowed individuals to imitate adaptive survival and social skills (Boyd & Richerson, 1985; Henrich & Boyd, 1998). Adding to this, perceived threats cause changes in our internal body state, such as heart rate, breathing rate, and attentional arousal. Generally, people engage with environmental stimuli that provoke this type of sympathetic arousal (Kensinger, 2007). To this point, one researcher showed that narratives that developed tension through a plot could sustain readers’ attention for longer (Zak, 2014).
Second, literary devices can increase the number of cognitive resources devoted to a narrative while attending, processing, and encoding it to memory, thus increasing its likelihood of adoption and engagement. Questions entice us to formulate a response, thus increasing our level of processing (Swasy & Munch, 1985). Including paradoxes can drive someone towards using higher levels of processing to deconflict information and meaning (Epley & Eyre, 2007; Kahneman, 2011). Other language-based traits are metaphors and analogies, which borrow terms and associations from a well-known domain to help explain another domain.
Third, anomalous stimuli and recognizable information are more likely to gain our attention and encourage deeper processing for memory retention. People are more likely to notice and remember anomalous information – this is known as the Von Restorff Effect (Ashcraft & Radvansky, 2009). Alternatively, top-down processing guided by previous memories can contribute to over-emphasis of quickly and easily processed information – this is known as the ease of processing heuristic (Kornell et al., 2011). Familiar and frequently encountered information can elicit this heuristic, thus contributing to a confirmational bias towards previous judgments and preformed schemas.
Fourth, while the content of two messages can be identical, the way a transmitter frames the message can evoke different emotional states – this framing difference can contribute to varying levels of engagement likelihood (Rothman et al., 1999). In one example, research participants read a statistic about German shepherd attacks as either “they are loyal, intelligent, and safe dogs, with other breeds accounting for 89% of dog attacks” or “they are dangerous dogs and account for 11% of all dog attacks” (Fessler et al., 2014). When researchers asked participants to rate the believability of each message, they tended to remember and rate negatively framed facts as more likely to be true.
Finally, humans hate uncertainty about their environment. In our evolutionary past, the ability to make predictions was frequently the difference between life and death. If you were unsure whether the twigs breaking is an approaching predator, you are too late. Because of this, we can think of our brains as “inference-generating machines” that constantly attempt to predict the future through past experiences and current sensory information. Some cognitive scientists call this premembering experience (Nobre & Stokes, 2019). In other words, we attend to and remember information based on what we think will be important in the future. Since preformed mental models primarily drive simulated futures, we tend to search for information that validates our future projections and current mental models.
First, the order in which we receive narratives and information can significantly impact what things people engage with. To start, we are inclined to adopt the first solution that satisfies some predetermined criteria; this is known as the satisficing heuristic (Simon, 1956). Follow-on research shows that the main satisficing criterion often is whether the information confirms preconceived notions, especially when imagining cause-and-effect relationships (Chapman, 1967). Additionally, individuals often use early information to make judgments; a decision-making approach called the anchoring bias (Tversky & Kahneman, 1974). The primacy effect magnifies this tendency, which describes the ability to recall early items in a list more accurately than later items (Murdock, 1962). Anchoring bias is so entrenched as a part of cognitive processing that experimenters studying it still observe the effect in subjects even after being made aware of and warned of the bias (Tversky & Kahneman, 1972).
Second, fuzzy-trace theory is a theory of cognition used to explain probability judgments, risk perception, decision-making, and memory (Broniatowski & Reyna, 2018). According to fuzzy-trace theory, narratives are encoded into long-term memory through verbatim and gist representations (Reyna et al., 2016). A verbatim representation encodes a decontextualized version of events in an objective manner. In contrast, the gist representation encodes a meaningful and subjective interpretation of an event while forming connections to previously formed schema and mental models. Notably, gist interpretations tend to influence behavior rather than verbatim representations (Reyna & Adam, 2003).
Third, narratives that contain contextualized information similar to preexisting mental models are more likely to spread; this is known as schema congruency. Individuals tend to associate the ease with which we bring an example to mind with the likelihood of that example’s existence – this is known as the availability heuristic (Ashcraft & Radvansky, 2009). Additionally, the easier it is to imagine the narrative being true, the more likely it will be adopted – this simulation heuristic is in response to narratives that are most similar to existing mental models and schema (Evans, 1989). Finally, when faced with ambiguous and uncertain alternatives, we tend to choose the one most recognizable to us – this is known as the recognition heuristic (Gigerenzer & Gaissmaier, 2011).
Fourth, establishing anchoring stimuli can alter a person’s perceptual or judgment process; this is known as contrast effects. Cialdini et al., show this effect in their door-in-the-face technique (1975). The basis of this technique is that people are more likely to accept a minor request if they initially turn down a larger one. Additionally, a technique known as foot-in-the-door first asks a person to accept a modest request before making a larger request. Most people would reject the larger request if asked on its own. However, since the larger request is an incremental step from the modest request, more people will agree to the larger request if first asked to accept a modest request. Two social theories explain these techniques’ success. First, Bem’s self-perception theory states we develop attitudes by viewing our own actions (e.g., “I put a sign in my window, I must care about safe driving”) (1967). Festinger’s cognitive dissonance theory compounds this, which states that people want their attitudes, behaviors, and feelings to remain consistent and unchanging (1957; Brehm & Cohen, 1962). In other words, people tend to form attitudes based on their actions or previously developed attitudes.
Finally, repeated exposure to narratives, distributed across time, and transmitted through multiple sources can increase its likelihood of spread and adoption. Researchers have observed that repeated interaction and exposure to information, known as the mere-exposure effect, can positively affect memory storage and someone’s attitude towards it. Scientists observe this effect across stimuli, cultures, and species (Zajonc, 2001). This process is boosted by recognition memory (the ability to identify familiar stimuli) and leads to faster, more accurate informational recall (Jia et al., 2020). Researchers suggest elaborating and repeating different aspects of an argument to keep a person’s interest (Allen & Preiss, 1998; O’Keefe, 1999). Additionally, varying the source of the same message increases memory and persuasive power (Harkins & Petty, 1987). Also, scientists have found that distributed exposure across a time range will lead to better memory encoding (Gerbier & Toppino, 2015; Glenberg & Lehmann, 1980).
First, narratives with “social utility” or those that contain information providing a positive image within important social groups are likely to spread. Impression management theory states that people try to regulate their perception by controlling and communicating social information (Tedeschi & Rosenfeld, 1981). Particularly, people want to paint a positive image of themselves to others. Because of this, we are susceptible to persuasive attempts that offer to enhance our self-image.
Second, normative social influence and informational social influence affect our decision-making, especially through groups that are important to us. When individuals want to be accepted into a group, they are influenced by “normative social influence.” Additionally, when a group is unanimous in its support, people feel more significant pressure to conform. To this end, individuals tend to adopt narratives from groups that are relatively important to an individual, close in proximity, relatively large, and unanimous in opinion (Asch, 1956). Additionally, people will use group cues or consult with people having “expertise” on the situation when forming their own opinion about an ambiguous, complicated, or crisis situation; this is known as informational social influence. A famous example of this was a study in which experimenters showed participants a perpetrator before asking them to identify the perpetrator in a police-line up (Baron et al., 1996). Unknowing to the participant, experimenters changed the perpetrator’s clothes between the initial picture and the police line-up. They also added a time constraint to the experiment. Now the experimenters told half of the participants their performance would be used to establish a standard in future court cases – the other half were offered 20$ for a correct answer. Finally, the participant conducted the experiment with three other confederates that were playing the role of other participants. Each confederate was instructed to provide the wrong answer. In the group where participants could win $20, 35% of participants conformed to the wrong answers offered by the confederates. In the group whose performance would be used in future cases, over 50% of participants conformed to the wrong answer.
Third, people use group norms as reference points for attitude formation and decision-making. Group norms can influence people’s attitudes by changing the valence (positive or negative), the strength (strong or weak), and awareness (implicit or explicit) (Fazio et al., 2004; Holland et al., 2002; Hoffman et al., 2005). Using the group’s attitudes to form our attitudes is a component of social judgement theory. This theory suggests that people separate their attitudes into three categories, latitude of acceptance, latitude of rejection, and latitude of non-commitment (Sherif & Hovland, 1961). Latitudes of acceptance is the range of ideas and narratives deemed acceptable and reasonable. Narratives and ideas grouped in the acceptable latitude are used as context reference points, promoting attitude assimilation. Opposing these is the latitude of rejection, or the group of ideas deemed objectionable and unacceptable. Narratives and ideas grouped in the unacceptable latitude are used as comparison reference points, promoting attitude contrast. Individuals commonly use this continuum as reference points when making decisions, establishing attitudes, and adopting narratives.
Fourth, we seek information from two types of individuals: those with prestige and credibility. Prestigious individuals are those to whom many freely confer attention towards (Henrich & Gil-White, 2001). People assume that if many others are giving attention to someone or something, they should too (especially if the others giving attention are socially similar). Credible individuals are those who display mastery of locally valued skills (O’Keefe, 2002). An innate bias towards cues of prestige and credibility is so ingrained within us that we see this behavior in children and even babies. Children who viewed other children gazing at a toy were 13x more likely to use it (Chudek, 2012). Children are 4x more likely to prefer foods after witnessing others focusing on the food. Children as young as 18 months show preferential attention towards adults who correctly label known objects (as opposed to adults who deliberately mislabeled those objects) (Loenig, Clement, & Harris, 2004; Koenig & Echols, 2003).
Finally, transmission filters impact which narratives can spread across social media platforms. First, social communities “pre-filter” the narratives that reach an individual; this is known as a social transmission filter (Lyons & Kashima, 2001; 2003). That is to say, stories and narratives with meanings similar to group stereotypes and shared schemas are more often transmitted. The inverse is true for stories and narratives with meanings that oppose stereotypes and schemas. Additionally, this means within single narratives, specific details and events are only communicated if they are consistent with group stereotypes and schema. This narrative modification results in a phenomenon known as stereotype-consistency bias, in which people are more likely to transmit stereotype-consistent information and narratives (Clark & Kashima, 2007; Lyons & Kashima, 2006). This stereotype-consistent transmission bias is intensified through memory biases when recalling stereotype-specific information (Fyock & Stangor, 1994). Researchers have found that we are more likely to accurately remember and transmit information aligned with our stereotypes and schema. These transmission filters and resulting biases can contribute to online echo chambers and isolated communities because only narratives that are consistent with existing mental models are filtered through and freely transmitted.
To conclude, people use narratives to interpret the world around them. Because the modern socio-technical landscape enables unprecedented narrative spread, many interpretations of current and past events are circulating around the narrative landscape. Fortunately, we have insight into what leads someone to attend to, consume, remember, and transmit a narrative, in turn forming meaningful interpretations of the world. We can use this knowledge to promote beneficial and healthy narratives, discourage and prevent harmful narratives, and build meaningful and accurate narratives for others to use while making sense of the world.
Steven Davic is currently a Systems Engineer in MITRE’s National Security Engineering Center and a graduate student in Johns Hopkins University’s M.S. Data Analytics and Policy program. Broadly speaking, he is interested in understanding, forecasting, and shaping the human dimension as it relates to national security and military objective. Past experiences at the Marine Corps’ 1st Battalion 8th Marines, Marine Corps Warfighting Laboratory, Group W, Collegium Civitas’ Terrorism Research Center, Journal of European and American Intelligence Studies, and UMD’s Applied Research Laboratory for Intelligence and Security have allowed him to explore various dimensions of this interest. As an honors student, he studied intelligence analysis, psychology, and biological anthropology at James Madison University where he graduate Summa Cum Laude. He is also a member of the Information Professionals Association.
Allen, M., & Preiss, R. W. (Eds.). (1998). Persuasion: Advances through meta-analysis. Hampton Press (NJ).
Asch, S. E. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs: General and Applied, 70(9), 1–70. https://doi.org/10.1037/h0093718
Ashcraft, M. H., & Radvansky, G. A. (2009). Cognition 5th Edition.
Baron, R. S., Vandello, J. A., & Brunsman, B. (1996). The forgotten variable in conformity research: Impact of task importance on social influence. Journal of Personality and Social Psychology, 71(5), 915–927. https://doi.org/10.1037/0022-3518.104.22.1685
Bem, D. J. (1967). Self-perception: An alternative interpretation of cognitive dissonance phenomena. Psychological Review, 74(3), 183–200. https://doi.org/10.1037/h0024835
Boyd, R., & Richerson, P. J. (1985). Culture and the Evolutionary Process. Chicago: University of Chicago Press.
Brehm, J. W., & Cohen, A. R. (1962). Explorations in cognitive dissonance. John Wiley & Sons Inc. https://doi.org/10.1037/11622-000
Broniatowski, D. A., & Reyna, V. F. (2018). A formal model of fuzzy-trace theory: Variations on framing effects and the Allais paradox. Decision, 5(4), 205.
Chapman, L. J. (1967). Illusory correlation in observational report. Journal of Verbal Learning and Verbal Behavior, 6(1), 151-155.
Chudek, M., Heller, S., Birch, S., & Henrich, J. (2012). Prestige-biased cultural learning: Bystander’s differential attention to potential models influences children’s learning. Evolution and Human Behavior, 33(1), 46-56.
Cialdini, R. B., Vincent, J. E., Lewis, S. K., Catalan, J., Wheeler, D., & Darby, B. L. (1975). Reciprocal concessions procedure for inducing compliance: The door-in-the-face technique. Journal of personality and Social Psychology, 31(2), 206.
Clark, A. E., & Kashima, Y. (2007). Stereotypes help people connect with others in the community: a situated functional analysis of the stereotype consistency bias in communication. Journal of personality and social psychology, 93(6), 1028–1039. https://doi.org/10.1037/0022-3522.214.171.1248
Epley, N., & Eyre, R. N. (2007). Overcoming intuition: metacognitive difficulty activates analytic reasoning. Journal of experimental psychology: General, 136(4), 569.
Evans, J. S. B. (1989). Bias in human reasoning: Causes and consequences. Lawrence Erlbaum Associates, Inc.
Fazio, R. H., Eiser, J. R., & Shook, N. J. (2004). Attitude formation through exploration: Valence asymmetries. Journal of Personality and Social Psychology, 87(3), 293–311. https://doi.org/10.1037/0022-35126.96.36.1993
Fessler, D. M., Pisor, A. C., & Navarrete, C. D. (2014). Negatively-biased credulity and the cultural evolution of beliefs. PloS one, 9(4), e95167. https://doi.org/10.1371/journal.pone.0095167
Festinger, L. (1957). A theory of cognitive dissonance (Vol. 2). Stanford university press.
Fyock, J., & Stangor, C. (1994). The role of memory biases in stereotype maintenance. British Journal of Social Psychology, 33(3), 331–343. https://doi.org/10.1111/j.2044-8309.1994.tb01029.x
Gerbier, E., & Toppino, T. C. (2015). The effect of distributed practice: Neuroscience, cognition, and education. Trends in Neuroscience and Education, 4(3), 49-59.
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482. https://doi.org/10.1146/annurev-psych-120709-145346
Glenberg, A. M., & Lehmann, T. S. (1980). Spacing repetitions over 1 week. Memory & Cognition, 8(6), 528-538.
Harkins, S. G., & Petty, R. E. (1987). Information utility and the multiple source effect. Journal of personality and social psychology, 52(2), 260.
Henrich, J., & Boyd, R. (1998). The evolution of conformist transmission and the emergence of between-group differences. Evolution and human behavior, 19(4), 215-241. https://doi.org/10.1016/S1090-5138(98)00018-X
Henrich, J., & Gil-White, F. J. (2001). The evolution of prestige: Freely conferred deference as a mechanism for enhancing the benefits of cultural transmission. Evolution and human behavior, 22(3), 165-196.
Hofmann, W., Gschwendner, T., & Schmitt, M. (2005). On Implicit-Explicit Consistency: the Moderating Role of Individual Differences in Awareness and Adjustment. European Journal of Personality, 19, 25-49. https://doi.org/10.1002/per.537
Holland, R. W., Verplanken, B., & Van Knippenberg, A. (2002). On the nature of attitude–behavior relations: The strong guide, the weak follow. European journal of social psychology, 32(6), 869-876.Homans, G. C. (1950). The human group. Harcourt, Brace.
Ingram, D. (2017, May 4). Facebook nears ad-only business model as game revenue falls, Reuters, retrieved from http://www.reuters.com/article/us-facebook-revenue
Jia, S., Hui, F., Li, S., Zhao, X., & Khattak, A. J. (2020). Long short‐term memory and convolutional neural network for abnormal driving behaviour recognition. IET Intelligent Transport Systems, 14(5), 306-312.
Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
Kensinger, E. A. (2007). Negative emotion enhances memory accuracy: Behavioral and neuroimaging evidence. Current Directions in Psychological Science, 16(4), 213-218.
Koenig, M. A., & Echols, C. H. (2003). Infants’ understanding of false labeling events: The referential roles of words and the speakers who use them. Cognition, 87(3), 179-208.
Koenig, M. A., Clément, F., & Harris, P. L. (2004). Trust in testimony: Children’s use of true and false statements. Psychological Science, 15(10), 694-698.
Kornell, N., Rhodes, M. G., Castel, A. D., & Tauber, S. K. (2011). The ease-of-processing heuristic and the stability bias: dissociating memory, memory beliefs, and memory judgments. Psychological science, 22(6), 787–794. https://doi.org/10.1177/0956797611407929
Lyons, A. & Kashima, Y. (2001). The reproduction of culture: Communication processes tend to maintain cultural stereotypes. Social Cognition, 19, 372– 394.
Lyons, A. & Kashima, Y. (2003). How are stereotypes maintained through communication? The influence of stereotype sharedness. Journal of Personality and Social Psychology, 85, 989– 1005.
Lyons, A., & Kashima, Y. (2006). Maintaining stereotypes in communication: Investigating memory biases and coherence‐seeking in storytelling. Asian Journal of Social Psychology, 9(1), 59-71. https://doi.org/10.1111/j.1467-839X.2006.00184.x
Murdock, B. B., Jr. (1962). The serial position effect of free recall. Journal of Experimental Psychology, 64(5), 482–488. https://doi.org/10.1037/h0045106
Nairne, J. S., & Pandeirada, J. N. (2016). Adaptive Memory: The Evolutionary Significance of Survival Processing. Perspectives on psychological science : a journal of the Association for Psychological Science, 11(4), 496–511. https://doi.org/10.1177/1745691616635613
Nobre, A.C., & Stokes, M.G. (2019). Premembering Experience: A Hierarchy of Time-Scales for Proactive Attention. Neuron, 104, 132 – 146.
O’Keefe, D. J. (1999). How to handle opposing arguments in persuasive messages: A meta-analytic review of the effects of one-sided and two-sided messages. Annals of the International Communication Association, 22(1), 209-249.
O’Keefe, D. J. (2002). Persuasion: Theory and research. (2nd ed.) Sage Publications.
Reyna, V. F., & Adam, M. B. (2003). Fuzzy-Trace Theory, Risk Communication, and Product Labeling in Sexually Transmitted Diseases. Risk Analysis, 23(2), 325–342.
Reyna, V. F., Corbin, J. C., Weldon, R. B., & Brainerd, C. J. (2016). How fuzzy-trace theory predicts true and false memories for words, sentences, and narratives. Journal of Applied Research in Memory and Cognition, 5(1), 1–9.
Rothman, A. J., Martino, S. C., Bedell, B. T., Detweiler, J. B., & Salovey, P. (1999). The systematic influence of gain-and loss-framed messages on interest in and use of different types of health behavior. Personality and Social Psychology Bulletin, 25(11), 1355-1369. doi:10.1177/0146167299259003
Sherif, M., & Hovland, C. I. (1961). Social judgment: Assimilation and contrast effects in communication and attitude change. Yale Univer. Press.
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological review, 63(2), 129.
Swasy, J. L., & Munch, J. M. (1985). Examining the target of receiver elaborations: Rhetorical question effects on source processing and persuasion. Journal of Consumer Research, 11(4), 877–886. https://doi.org/10.1086/209023
Tedeschi, J.T. and Riess, M. (1981) Impression Management Theory and Social Psychological Research. Academic, New York.
Tversky, A., & Kahneman, D. (1972). Anchoring and calibration in the assessment of uncertain quantities. Oregon Research Institute Research Bulletin, 12(5).
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232. https://doi.org/10.1016/0010-0285(73)90033-9
Zajonc, R. B. (2001). Mere Exposure: A Gateway to the Subliminal. Current Directions in Psychological Science, 10(6), 224–228. https://doi.org/10.1111/1467-8721.00154
Zak, P. (2014, October 28). Communication why your brain loves good storytelling. Harvard Business Review. Retrieved November 18, 2021, from https://www.paladinww.com/uploads/6/5/0/8/65089471/why_your_brain_loves_good_storytelling__1_.pdf