We first use the data from the first experiment to characterize the initial configuration of the system before any social influence occurs, that is, how opinions are initially distributed and how the accuracy and confidence of the answers are correlated with each other.
As shown in the example in Fig. 1A, the initial distribution of opinions has a lognormal shape, with a typical long tail indicating the significant presence of outliers. For each of 32 items we performed a Kolmogorov-Smirnov normality test of log(Oi), where Oi is the initial opinion of individual i. The test yielded p-values above.05 for 84% of the items, indicating that the null hypothesis cannot be rejected at the 5% significance level for these items. The remaining 16% still had reasonably high p-values (always >10−3), suggesting that the initial opinions Oi indeed follow a lognormal distribution.
Figure 1. The initial configuration of the system in the absence of social influence.
(A) Initial distribution of opinions for one representative example question (see Fig. S1 for an overview of all 32 items). The normalized answer corresponds to the estimate of the participants divided by the true value (i.e., 660°C for this question). The red curve shows the best fit of a lognormal distribution. The green dots at the top indicate the location of estimates associated with high confidence levels (). One of them constitutes an outlier. (B) Accuracy of participants’ answers as a function of their confidence level, as determined from the complete dataset (32 items).
We also analyzed the correlation between the confidence level of the participants and the accuracy of their answer (Fig. 1B). Interestingly, the confidence level is not such a reliable cue for accuracy . First, we found no significant correlation between an individual i’s confidence level Ci and the quality of his or her answer (a correlation test between Ci and the error where T is the true value yielded a coefficient of –.03). Nevertheless, a trend can be highlighted by grouping the data into classes of error ranges: very good answers (), good answers (), bad answers () and very bad answers (). As it can be seen from Fig. 1B, only the maximum confidence level Ci = 6 is a relevant indicator of the quality of the answer, leading to a good or very good estimate in 80% of the time. By contrast, lower confidence levels are less informative about accuracy. For instance, the second highest confidence value of Ci = 5 has a 39% chance to correspond to a bad or very bad estimate. Similarly, a value of Ci = 4 is more likely to accompany a bad or very bad estimate (53%) than a good or very good one (47%). The lowest confidence values Ci = 1 and Ci = 2 do not differ from each other. Taking the revised estimates of Experiment 2 into account, we observe that the reliability of high confidence judgments is undermined by social influence . As shown in Fig. 2B, the distribution of errors for very confident individuals (Ci = 5 or 6) becomes more noisy, widespread and clustered around certain values thus becoming less informative about accuracy after social influence.
To explore the wisdom of crowds, we compared the accuracy of various aggregating methods before and after social influence occurred (Fig. 2A). Our results agree with previous findings , . We find that the error distributions tend to become widespread, now covering a greater proportion of also high error values after social influence, regardless of the aggregating method.
Next, we focus on how people adjust their opinion after being informed about the opinion of another individual, which is the aim of Experiment 2. In agreement with previous studies , , our results show that two variables have an important influence on how the individual i revises his or her opinion when exposed to the opinion and confidence of another participant j: the difference in confidence values and the normalized distance between opinions: , where Oj and Cj represent the opinion and confidence level of participant j, respectively . To provide a visual, quantitative overview of the effects of social influence, we draw an influence map that illustrates the interplay of these two variables in the process of opinion adaptation (Fig. 3). For the sake of simplicity, we distinguish three possible heuristics :
1. Keep initial opinion, when individuals do not change their judgment after receiving a feedback, that is: Ri = Oi, where Ri is the revised opinion of participant i.
2. Make a compromise, when the revised opinion falls in between the initial opinion Oi and the feedback Oj: min(Oi, Oj)< Ri <max(Oi, Oj).
3. Adopt other opinion, when an individual i adopts the partner’s opinion: Ri = Oj.
The influence map shows the heuristic that is used by the majority of people as and change (Fig. 3A). Most of the data points (86% of 885) are found for and , which cover a large part of the influence map and seem to be reasonable ranges being also encountered in real life situations. At the edge of the map, however, the results are more uncertain due to the scarcity of available data points.
Figure 3A shows that the first and more conservative strategy tends to dominate the two others. In particular, the majority of people systematically keep their opinion when the value of is positive, that is, when their own confidence exceeds their partner’s . However, when their confidence level is equal or lower than their partner’s, individuals tend to adapt their opinion accordingly. Importantly, one can distinguish three zones in the influence map, according to the distance between estimates (Fig. 3B). First, when both individuals have a similar opinion (<0.3), individuals tend to keep their initial judgment, irrespective of their partner’s confidence. Moreover, they also have a strong tendency to increase their confidence level (see Fig. 4A indicating the changes in confidence). Therefore, we interpret this area of agreement as being a confirmation zone, where feedback tends to simultaneously reinforce initial opinions and increase an individual’s confidence.
Figure 4. The probability of increasing (red), decreasing (blue), or maintaining (green) the confidence level after social influence.
Changes in confidence are indicated according to the opinion distance classes as defined in the influence map (Fig. 3): (A) near when , (B) intermediate when , and (C) far when . A tendency to increase confidence is visible in the near and intermediate zones when participants interact with a more confident subject. Confidence can also decrease in the far zone, when .
It turns out that feedback has the strongest influence at intermediate levels of disagreement, when 0.3<<1.1. In this zone, the “compromise” heuristic is selected by most people when , and the “adoption” heuristic appears for lower values of . We call this the influence zone, where social influence is strongest. Here, the other’s opinion differs sufficiently from the initial opinion to trigger a revision but is still not far enough away to be completely ignored. In particular, the confidence level of the participants tends to remain the same after the interaction (Fig. 4B).
Finally, when the distance between opinions is very large (i.e., >1), the strength of social influence diminishes progressively . In this zone, people seem to pay little attention to the judgment of another, presumably assuming that it may be an erroneous answer. Nevertheless, the other’s opinion is not entirely ignored, as the majority of people still choose the “compromise” heuristic when the partner is markedly more confident (i.e. ). Moreover, people who are initially very confident (i.e. ) presumably begin to doubt the accuracy of their judgment and exhibit a high likelihood (of almost 70%) of reducing their confidence level. Even more remote opinions are likely to be ignored entirely, but as this situation rarely occurs our data does not warrant a reliable conclusion here.
Taking these empirical regularities into account, we now elaborate an individual-based model of opinion adaptation and explore the collective dynamics of opinion change when many people influence each other repeatedly. To this end, we first describe the above influence map by means of a simplified diagram showing the heuristics that are used by most individuals according to and (Fig. 3B). Alternatively, the same diagram can be characterized as a decision tree (Fig. 3C). The model is defined as follows:
First, an individual notes the distance between his or her own and a partner’s opinion and classifies it as near, far, or at an intermediate distance. For this, we used two threshold values of and , assuming that the feedback is near when <, far when >, and at an intermediate distance otherwise. The numerical values of and were determined empirically from the influence map. Second, the individual considers the difference in confidence values to choose among the three heuristics. Again, we define two threshold values and and assume that the individual decides to “keep own opinion” if , to “adopt other opinion” if , and to “make a compromise” otherwise. The three strategies can be formally defined as , where the parameter delineates the strength of social influence. Therefore, we have when the individual decides to “keep own opinion”, and when the individual decides to “adopt”. When the individual chooses the “compromise” strategy, that is when , the average weight value as measured from our data equals to (SD = 0.24), indicating that people did not move exactly between their initial estimate and the feedback (which would correspond to a weight value of 0.5), but exhibited a bias toward their own initial opinion . Over all our data points, 53% correspond to the first strategy (), 43% to the second (), and 4% to the third ().
The values of and depend on the distance zone defined before:
- When is small, the other’s opinion constitutes a confirmation of the initial opinion. According to our observations, = -5 and = -6. Additionally, the confidence level Ci is increased by one point if . As indicated by Fig. 4A, Ci is also increased by one point with a probability p = 0.5 when , and remains the same otherwise.
- When is intermediate, the feedback has a significant influence on the subject’s opinion. In this case, we set = 0 and = -3. The data shows that the confidence level is changed only if (Fig. 4B). In this case, Ci increases with probability p = 0.5, and remains the same otherwise.
- When is large, the thresholds are set to = -2 and = -6. This time, the confidence level decreases by one point when , and remains the same otherwise.
Here, all the parameter values were directly extracted from the observations (Fig.3B and Fig.4).
Having characterized the effects of social influence at the individual level, we now scale up to the collective level and study how repeated influences among many people play out at the population scale. Because the macroscopic features of the system are only visible when a large number of people interact many times, it would be extremely difficult to investigate this under laboratory conditions. Therefore, we conducted a series of numerical simulations of the above model to investigate the collective dynamics of the system.
The initial conditions of our simulations correspond to the exact starting configurations observed in our experiments (i.e., the precise opinion and confidence values of all 52 participants observed in the first experiment) . In each simulation round, the 52 individuals are randomly grouped into pairs, and both individuals in a pair update their opinions according to the opinion of the other person, as predicted by our model. Thus, each individual is both a source and the target of social influence. We performed N = 300 rounds of simulated interactions, where N has been chosen large enough to ensure that the system has reached a stationary state. Here, we make the assumption that the decision tree that has been extracted from our experiment remains the same over repeated interactions. This assumption is reasonable to the extent that the outcome of the decision tree (i.e. the strategy that is chosen) depends on the confidence level of the individual, which is expected to change as people receive new feedback. In such a way, the strategies that will be selected by individuals are connected to the individual history of past interactions.
Fig. 5 shows the dynamics observed for three representative examples of simulations. Although a certain level of opinion fragmentation still remains, a majority of individuals converge toward a similar opinion. As shown by the arrow maps in Fig.5, the first rounds of the simulation exhibit important movements of opinions among low-confidence individuals (as indicated by the large horizontal arrows for confidence lower than 3), without increase of confidence (as shown in Fig. S2). After a certain number of rounds, however, a tipping point occurs at which a critical proportion of people meet up in the same region of the opinion space. This creates a subsequent increase of confidence in this zone, which in turn becomes even more attractive to others. This results in a positive reinforcement loop, leading to a stationary state in which the majority of people end up sharing a similar opinion. This amplification process is also marked by a sharp transition of the system’s global confidence level (Fig. S2), which is a typical signature of phase transitions in complex systems .
Figure 5. Three representative examples of the collective dynamics observed in the computer simulations.
For each example, the initial opinion map is shown on the left-hand side (experimental data), and the final opinion map after N = 300 rounds of simulations on the right-hand side. The opinion maps represent the proportion of individuals with a given opinion (x-axis) and a given confidence level (y-axis). As in Fig. 1, the normalized opinion is the actual opinion divided by the true value. The correct answer is represented by the red dashed lines (corresponding to a value of 1). Outliers with normalized opinion greater than 2 are not shown. The arrow maps represent the average movements over both opinion and confidence dimensions during simulations. Examples 1, 2, and 3 correspond to the questions “What is the length of the river Oder in kilometers? ”, “How many inhabitants has the East Frisian island Wangerooge?”, and “How many gold medals were awarded during the Olympics in China in 2008?”, respectively. The final convergence point may be determined by a dense cluster of low confidence individuals, as illustrated by Example 2 (majority effect), or by a few very confident individuals as in Example 3 (expert effect).
An intriguing finding of our simulations is that the collective opinion does not converge toward the average value of initial opinions (a correlation test yields a nonsignificant effect with a coefficient c = -.05). The correlation between the convergence point and the median value of the initial opinions is significant (p = .03) but the relatively moderate correlation coefficient c = 0.46 suggests that this relation remains weak. Likewise, the system does not systematically converge toward or away from the true value (nonsignificant effect with a coefficient c = .11). Instead, the simulations exhibit complex collective dynamics in which the combined effect of various elements can drive the group in one direction or another. In agreement with previous works , the collective outcome appears to be poorly predictable and strongly dependent on the initial conditions . Nevertheless, we identified two major attractors of opinions that exert an important social influence over the group:
- The first attractor is the presence of a critical mass of uncertain individuals who happen to share a similar opinion. In fact, when such a cluster of individuals is initially present in the system—either by chance or because individuals share a common bias—the rest of the crowd tends to converge toward it, as illustrated by Fig. 5-Example2. This majority effect is typical of conformity experiments that have been conducted in the past , where a large number of people sharing the same opinion have a strong social influence on others.
- The second attractor is the presence of one or a few highly confident individuals, as illustrated by Fig. 5-Example3. The origin of this expert effect is twofold: First, very confident individuals exert strong persuasive power, as shown by the influence map. Second, unconfident people tend to increase their own confidence after interacting with a very confident person, creating a basin of attraction around that person’s opinion , .
Our simulations show that the majority effect and the expert effect are not systematically beneficial to the group, as both attractors could possibly drive the group away from the truth (Fig. 5-Example 2). What happens in the case of conflicting interests, when the expert and the majority effects apply simultaneously and disagree with each other (Fig. 5-Example 3)? To investigate this issue, we conducted another series of simulations in which a cluster of low-confidence individuals sharing the same opinion Omaj, is facing a minority of high-confidence experts holding another opinion Oexp. As shown by Fig. 6A, the majority effect overcomes the expert effect when the proportion of experts pExp is lower than a certain threshold value located around 10%. However, as pExp increases from 10%, to 20% a transition occurs and the convergence point shifts from the majority to the experts’ opinion. Remarkably, this transition point remains stable even when a proportion pNeut of neutral individuals (defined as people with random opinions and a low confidence level) are present in the system (Fig. 6B). As pNeut increases above 70%, however, noise gradually starts to dominate, leading the expert and the majority effects to vanish. The tipping point occurring at a proportion of around 15% of experts appears to be a robust prediction, not only because it resists to a large amount of system noise (Fig. 6B), but also because a previous theoretical study using a completely different approach also reached a similar conclusion .
Figure 6. Which attractor dominates when the majority effect and the expert effect apply simultaneously?
(A) The evolution of collective opinion when varying the relative proportion of experts pExp, holding an opinion Oexp and a high confidence level Cexp = 6, and the proportion of people in the majority group pmaj holding an opinion Omaj and a low confidence level randomly chosen in the interval Cmaj = [1 3]. Here, the number of neutral individuals is fixed to pNeut = 0. (B) Phase diagram showing the parameter space where the majority or the expert effects applies, when increasing the proportion of neutral individuals pNeut holding a random opinion and a low confidence level randomly chosen in the interval Cuni = [1 3]. The schematic regions delimited by black or white dashed lines show the zones where the collective opinion converges toward the majority or the expert opinion, respectively. In the transition zone, the collective opinion converges somewhere between Oexp and Omaj. In some rare cases, the crowd splits into two groups or more.
Social influence occurs when a person's emotions, opinions, or behaviors are affected by others. Social influence takes many forms and can be seen in conformity, socialization, peer pressure, obedience,leadership, persuasion, sales, and marketing. In 1958, Harvard psychologist Herbert Kelman identified three broad varieties of social influence.
- Compliance is when people appear to agree with others but actually keep their dissenting opinions private.
- Identification is when people are influenced by someone who is liked and respected, such as a famous celebrity.
- Internalization is when people accept a belief or behavior and agree both publicly and privately.
Morton Deutsch and Harold Gerard described two psychological needs that lead humans to conform to the expectations of others. These include our need to be right (informational social influence) and our need to be liked (normative social influence). Informational influence (or social proof) is an influence to accept information from another as evidence about reality. Informational influence comes into play when people are uncertain, either because stimuli are intrinsically ambiguous or because there is social disagreement. Normative influence is an influence to conform to the positive expectations of others. In terms of Kelman's typology, normative influence leads to public compliance, whereas informational influence leads to private acceptance.
Social Influence is a broad term that relates to many different phenomena. Listed below are some major types of social influence that are being researched in the field of social psychology. For more information, follow the main article links provided.
There are three processes of attitude change as defined by Harvard psychologist Herbert Kelman in a 1958 paper published in the Journal of Conflict Resolution. The purpose of defining these processes was to help determine the effects of social influence: for example, to separate public conformity (behavior) from private acceptance (personal belief).
Main article: Compliance (psychology)
Compliance is the act of responding favorably to an explicit or implicit request offered by others. Technically, compliance is a change in behavior but not necessarily in attitude; one can comply due to mere obedience or by otherwise opting to withhold private thoughts due to social pressures. According to Kelman's 1958 paper, the satisfaction derived from compliance is due to the social effect of the accepting influence (i.e., people comply for an expected reward or punishment-aversion).
Main article: Identification (psychology)
Identification is the changing of attitudes or behaviors due to the influence of someone who is admired. Advertisements that rely upon celebrity endorsements to market their products are taking advantage of this phenomenon. According to Kelman, the desired relationship that the identifier relates to the behavior or attitude change.
Main article: Internalization
Internalization is the process of acceptance of a set of norms established by people or groups that are influential to the individual. The individual accepts the influence because the content of the influence accepted is intrinsically rewarding. It is congruent with the individual's value system, and according to Kelman the "reward" of internalization is "the content of the new behavior".
Main article: Conformity
Conformity is a type of social influence involving a change in behavior, belief, or thinking to align with those of others or with normative standards. It is the most common and pervasive form of social influence. Social psychology research in conformity tends to distinguish between two varieties: informational conformity (also called social proof, or "internalization" in Kelman's terms ) and normative conformity ("compliance" in Kelman's terms).
In the case of peer pressure, a person is convinced to do something that they might not want to do (such as taking illegal drugs) but which they perceive as "necessary" to keep a positive relationship with other people (such as their friends). Conformity from peer pressure generally results from identification with the group members or from compliance of some members to appease others.
Conformity can be in appearance, or may be more complete in nature; impacting an individual both publicly and privately.
Compliance (also referred to as acquiescence) demonstrates a public conformity to a group majority or norm, while the individual continues to privately disagree or dissent, holding on to their original beliefs or to an alternative set of beliefs differing from the majority. Compliance appears as conformity, but there is a division between the public and the private self.
Conversion includes the private acceptance that is absent in compliance. The individual's original behaviour, beliefs, or thinking changes to align with that of others (the influencers), both publicly and privately. The individual has accepted the behavior, belief, or thinking, and has internalized it, making it his own. Conversion may also refer to individual members of a group changing from their initial (and varied) opinions to adopt the opinions of others, which may differ from their original opinions. The resulting group position may be a hybrid of various aspects of individual initial opinions, or it may be an alternative independent of the initial positions reached through consensus.
What appears to be conformity may in fact be congruence. Congruence occurs when an individual's behavior, belief, or thinking is already aligned with that of the others, and no change occurs.
In situations where conformity (including compliance, conversion, and congruence) is absent, there are non-conformity processes such as independence and anti-conformity. Independence, also referred to as dissent, involves an individual (either through their actions or lack of action, or through the public expression of their beliefs or thinking) being aligned with their personal standards but inconsistent with those of other members of the group (either all of the group or a majority). Anti-conformity, also referred to as counter-conformity, may appear as independence, but it lacks alignment with personal standards and is for the purpose of challenging the group. Actions as well as stated opinions and beliefs are often diametrically opposed to that of the group norm or majority. The underlying reasons for this type of behavior may be rebelliousness/obstinacy or it may be to ensure that all alternatives and view points are given due consideration.
Main article: Minority influence
Minority influence takes place when a majority is influenced to accept the beliefs or behaviors of a minority. Minority influence can be affected by the sizes of majority and minority groups, the level of consistency of the minority group, and situational factors (such as the affluence or social importance of the minority). Minority influence most often operates through informational social influence (as opposed to normative social influence) because the majority may be indifferent to the liking of the minority.
Main article: Self-fulfilling prophecy
A self-fulfilling prophecy is a prediction that directly or indirectly causes itself to become true due to positive feedback between belief and behavior. A prophecy declared as truth (when it is actually false) may sufficiently influence people, either through fear or logical confusion, so that their reactions ultimately fulfill the once-false prophecy. This term is credited to sociologistRobert K. Merton from an article he published in 1948.
Main article: Reactance (psychology)
Reactance is the adoption of a view contrary to the view that a person is being pressured to accept, perhaps due to a perceived threat to behavioral freedoms. This phenomenon has also been called anticonformity. While the results are the opposite of what the influencer intended, the reactive behavior is a result of social pressure. It is notable that anticonformity does not necessarily mean independence. In many studies, reactance manifests itself in a deliberate rejection of an influence, even if the influence is clearly correct.
Main article: Obedience (human behavior)
Obedience is a form of social influence that derives from an authority figure. The Milgram experiment, Zimbardo's Stanford prison experiment, and the Hofling hospital experiment are three particularly well-known experiments on obedience, and they all conclude that humans are surprisingly obedient in the presence of perceived legitimate authority figures.
Main article: Persuasion
Persuasion is the process of guiding oneself or another toward the adoption of an attitude by rational or symbolic means. Robert Cialdini defined six "weapons of influence": reciprocity, commitment, social proof, authority, liking, and scarcity. These "weapons of influence" attempt to bring about conformity by directed means. Persuasion can occur through appeals to reason or appeals to emotion.
Main article: Psychological manipulation
Psychological manipulation is a type of social influence that aims to change the behavior or perception of others through abusive, deceptive, or underhanded tactics. By advancing the interests of the manipulator, often at another's expense, such methods could be considered exploitative, abusive, devious, and deceptive.
Social influence is not necessarily negative. For example, doctors can try to persuade patients to change unhealthy habits. Social influence is generally perceived to be harmless when it respects the right of the influenced to accept or reject it, and is not unduly coercive. Depending on the context and motivations, social influence may constitute underhanded manipulation.
Many factors can affect the impact of social influence.
Social impact theory
Main article: Social impact theory
Social impact theory was developed by Bibb Latané in 1981. This theory asserts that there are three factors which increase a person's likelihood to respond to social influence:
- Strength: The importance of the influencing group to the individual
- Immediacy: Physical (and temporal) proximity of the influencing group to the individual at the time of the influence attempt
- Number: The number of people in the group
Cialdini's "weapons of influence"
Robert Cialdini defines six "weapons of influence" that can contribute to an individual's propensity to be influenced by a persuader:
- Reciprocity: People tend to return a favor.
- Commitment and consistency: People do not like to be self-contradictory. Once they commit to an idea or behavior, they are averse to changing their minds without good reason.
- Social proof: People will be more open to things that they see others doing. For example, seeing others compost their organic waste after finishing a meal may influence the subject to do so as well.
- Authority: People will tend to obey authority figures.
- Liking: People are more easily swayed by people they like.
- Scarcity: A perceived limitation of resources will generate demand.
Social Influence is strongest when the group perpetrating it is consistent and committed. Even a single instance of dissent can greatly wane the strength of an influence. For example, in Milgram's first set of obedience experiments, 65% of participants complied with fake authority figures to administer "maximum shocks" to a confederate. In iterations of the Milgram experiment where three people administered shocks (two of whom were confederates), once one confederate disobeyed, only ten percent of subjects administered the maximum shocks.
Main article: Appeal to authority
See also: Reputation
Those perceived as experts may exert social influence as a result of their perceived expertise. This involves credibility, a tool of social influence from which one draws upon the notion of trust. People believe an individual to be credible for a variety of reasons, such as perceived experience, attractiveness, knowledge, etc. Additionally, pressure to maintain one's reputation and not be viewed as fringe may increase the tendency to agree with the group. This phenomenon is known as groupthink. Appeals to authority may especially affect norms of obedience. The compliance of normal humans to authority in the famous Milgram experiment demonstrate the power of perceived authority.
Those with access to the media may use this access in an attempt to influence the public. For example, a politician may use speeches to persuade the public to support issues that he or she does not have the power to impose on the public. This is often referred to as using the "bully pulpit." Likewise, celebrities don't usually possess any political power, but they are familiar to many of the world's citizens and, therefore, possess social status.
Power is one of the biggest reasons an individual feels the need to follow through with the suggestions of another. A person who possesses more authority (or is perceived as being more powerful) than others in a group is an icon or is most "popular" within a group. This person has the most influence over others. For example, in a child's school life, people who seem to control the perceptions of the students at school are most powerful in having a social influence over other children.
Culture appears to play a role in the willingness of an individual to conform to the standards of a group. Stanley Milgram found that conformity was higher in Norway than in France. This has been attributed to Norway's longstanding tradition of social responsibility, compared to France's cultural focus on individualism. Japan likewise has a collectivist culture and thus a higher propensity to conformity. However, a 1970 Asch-style study found that when alienated, Japanese students were more susceptible to anticonformity (giving answers that were incorrect even when the group had collaborated on correct answers) one third of the time, significantly higher than has been seen in Asch studies in the past.
While gender does not significantly affect a person's likelihood to conform, under certain conditions gender roles do affect such a likelihood. Studies from the 1950s and 1960s concluded that women were more likely to conform than men. But a 1971 study found that experimenter bias was involved; all of the researchers were male, while all of the research participants were female. Studies thereafter found that the likelihood to conform almost equal between the genders. Furthermore, men conformed more often when faced with traditionally feminine topics, and women conformed more often when presented with masculine topics. In other words, ignorance about a subject can lead a person to defer to "social proof".
Main article: Appeal to emotion
Emotion and disposition may affect an individual's likelihood of conformity or anticonformity. In 2009, a study concluded that fear increases the chance of agreeing with a group, while romance or lust increases the chance of going against the group.
Main article: Social network analysis
A social network is a social structure made up of nodes (representing individuals or organizations) which are connected (through ties, also called edges, connections, or links) by one or more types of interdependency (such as friendship, common interests or beliefs, sexual relations, or kinship). Social network analysis uses the lens of network theory to examine social relationships. Social network analysis as a field has become more prominent since the mid-20th century in determining the channels and effects of social influence. For example, Christakis and Fowler found that social networks transmit states and behaviors such as obesity, smoking, drinking and happiness.
Identifying the extent of social influence, based on large-scale observational data with a latent social network structure, is pertinent to a variety of collective social phenomena including crime, civil unrest, and voting behavior in elections. For example, methodologies for disentangling social influence by peers from external influences—with latent social network structures and large-scale observational data—were applied to US presidential elections,stock markets, and civil unrest.
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