Editing Information cascade (section)

=== Social networks and social media ===
{{see|social network analysis}}
Dotey et al.<ref>{{cite web|author=Dotey, A., Rom, H. and Vaca C.|title=Information Diffusion in Social Media|year=2011|publisher=Stanford University|url=https://snap.stanford.edu/class/cs224w-2011/proj/mrom_Finalwriteup_v1.pdf}}</ref> state that information flows in the form of cascades on the [[social network]]. According to the authors, analysis of [[viral phenomenon|virality]] of information cascades on a social network may lead to many useful applications like determining the most influential individuals within a network. This information can be used for ''maximizing market effectiveness'' or ''influencing [[public opinion]]''. Various structural and temporal features of a network affect cascade virality. Additionally, these models are widely exploited in the problem of [[Rumor spread in social network]] to investigate it and reduce its influence in online social networks.

In contrast to work on information cascades in social networks, the [[social influence]] model of [[belief spread]] argues that people have some notion of the private beliefs of those in their network.<ref name="Friedkin Johnsen 2009 p. ">{{cite book | last1=Friedkin | first1=Noah E. | last2=Johnsen | first2=Eugene C. | title=Social Influence Network Theory | publisher=Cambridge University Press | location=Cambridge | year=2009 | isbn=978-0-511-97673-5 | doi=10.1017/cbo9780511976735 }}</ref>  The social influence model, then, relaxes the assumption of information cascades that people are acting only on observable actions taken by others.  In addition, the social influence model focuses on embedding people within a social network, as opposed to a queue.  Finally, the social influence model relaxes the assumption of the information cascade model that people will either complete an action or not by allowing for a continuous scale of the "strength" of an agents belief that an action should be completed.

Information cascades can also restructure the social networks that they pass through. For example, while there is a constant low level of churn in social ties on [[Twitter]]—in any given month, about 9% of all social connections change—there is often a spike in follow and unfollow activity following an information cascade, such as the sharing of a viral tweet.<ref name=":0">{{cite book |doi=10.1145/2566486.2568043 |arxiv=1403.2732 |s2cid=6353961 |chapter=The bursty dynamics of the Twitter information network |title=Proceedings of the 23rd international conference on World wide web |year=2014 |last1=Myers |first1=Seth A. |last2=Leskovec |first2=Jure |pages=913–924 |isbn=978-1-4503-2744-2 }}</ref> As the tweet-sharing cascade passes through the network, users adjust their social ties, particularly those connected to the original author of the viral tweet: the author of a viral tweet will see both a sudden loss in previous followers and a sudden increase in new followers.

As a part of this cascade-driven reorganization process, information cascades can also create [[Assortative mixing|assortative social networks]], where people tend to be connected to others who are similar in some characteristic. Tweet cascades increase in the similarity between connected users, as users lose ties to more dissimilar users and add new ties to similar users.<ref name=":0" /> Information cascades created by news coverage in the media may also foster [[political polarization]] by [[Echo chamber (media)|sorting social networks along political lines]]: Twitter users who follow and share more polarized news coverage tend to lose social ties to users of the opposite ideology.<ref>{{cite journal |last1=Tokita |first1=Christopher K. |last2=Guess |first2=Andrew M. |last3=Tarnita |first3=Corina E. |title=Polarized information ecosystems can reorganize social networks via information cascades |journal=Proceedings of the National Academy of Sciences |date=14 December 2021 |volume=118 |issue=50 |pages=e2102147118 |doi=10.1073/pnas.2102147118 |pmc=8685718 |pmid=34876511 |bibcode=2021PNAS..11802147T |doi-access=free }}</ref>