Can social entropy theory explain social media?

Social entropy theory and analysis applied to communication

This is a learning module for the class Contemporary Social / Mass Media Theory taught at Purdue University by Sorin Adam Matei

Social entropy theory is derived from Shannon’s Mathematical Theory of Communication and it proposes that the evolution and structure of social systems at any scale can be studied using synthetic indicators, such as entropy. Such indicators can tell two main things: 1. what is the relative state of organization and 2. how much energy is available for social action for a given system. The explanatory power of social entropy theory is derived from the fact that its central indicator, social entropy, is at the same time a meaningful measure of information writ large. Social entropy was construed by Shannon, and discussed by his successors, as a measure of how “in-formed” (i.e., structured, organized, non-random) the world of symbols and social interactions is. As such, it is not just a simple indicator, but it can become a powerful explanatory tool, which can play the role of either independent or dependent analysis tool.


Shannon and Weaver, Mathematical Theory of Communication, 1949, Urbana, University of Illinois Press


This paper is written in three main sections. In the first and third, W. W. is responsible both for the ideas and the form. The middle section, namely “2), Communication Problems of Level A” is an interpretation of mathematical papers by Dr. Claude E. Shannon of the Bell Telephone Laboratories. Dr. Shannon’s work roots back, as von Neumann has pointed out, to Boltzmann’s observation, in some of his work on statistical physics (1894), that entropy is related to “missing information,” inasmuch as it is related to the number of alternatives which remain possible to a physical system after all the macroscopically observable information concerning it has been recorded. L. Szilard (Zsch. f. Phys. Vol. 53, 1925) extended this idea to a general discussion of information in physics, and von Neumann (Math. Foundation of Quantum Mechanics, Berlin, 1932, Chap. V) treated information in quantum mechanics and particle physics. Dr. Shannon’s work connects more directly with certain ideas developed some twenty years ago by H. Nyquist and R. V. L. Hartley, both of the Bell Laboratories; and Dr. Shannon has himself emphasized that communication theory owes a great debt to Professor Norbert Wiener for much of its basic philosophy. Professor Wiener, on the other hand, points out that Shannon’s early work on switching and mathematical logic antedated his own interest in this field; and generously adds that Shannon certainly deserves credit for independent development of such fundamental aspects of the theory as the introduction of entropic ideas. Shannon has naturally been specially concerned to push the applications to engineering communication, while Wiener has been more concerned with biological application (central nervous system phenomena, etc.)

Osgood, C., & Wilson, K. (1955). Some terms and associated measures for talking about communication. Urbana Champaign, IL: Institute of Communication Research.

The terms employed in information theory, as developed by Shannon, ‘Wiener and others (cf. 6, pp. 47-49, for a bibliography), provide at least the beginnings of an arbitrary metalanguage for talking about communication. They have the advantage of
extreme generality and. although the lexicon has to be extended a bit to cover human communication situations. the mechanistic nature of the language serves as a partial safeguard against unjustified implicit assumptions. This is particularly useful in a field that deals with human activities. like human communications. where everyone has a ready explanation of any phenomenon. but in terms from the lay language that are usually loaded with diverse and sometimes contradictory connotations. While it is not our intention to try to impose a new language, we have tried to select terms which avoid identification with any particular human communication situation (and thus pressure toward conviction by analogy). Also. we have tried to define these terms as precisely as possible. and we have associated these terms with a set of measurement operations. We have concluded that a relatively small number of basic measures can be used’ to describe a relatively large variety of communication situations, thus increasing the comparability and economy of such descriptions. This also means that. the lexicon is considerably larger than the number of measurement operations. It is important to point out at the outset. however. that what follows in this paper is not a predictive or explanatory theory of the human communication process but rather a descriptive model with measurement implications. This is for the most· part true of information theory as it has been applied to human communication situations. However. in developing any theory of human communication, such arbitrarily defined terms as used here can become the constructs whose interrelationships are specified by the principles of the theory, and the measurement operations associated with such terms can facilitate tests of the theory.

Kenneth Bailey
Kybernetes. London: 1997. Vol. 26, Iss. 6/7; pg. 674

A number of entropy models of social systems have been developed recently. Unfortunately, the complementarity of these approaches remains largely unanalyzed, due to terminological and conceptual differences among them.

Sorin A. Matei et al.
Collaboration and Communication in Online Environments: A Social Entropy Approach
Paper presented at the NCA Annual Conference, San Antonio, Texas, November, 2005

Sorin A. Matei et al.
Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated Collaborative Learning

A theoretically-grounded learning feedback tool suite, the Visible Effort (VE) Mediawiki extension, is proposed for optimizing online group learning activities by measuring the amount of equality and the emergence of social structure in groups that participate in Computer-Mediated Collaboration (CMC). Building on social entropy theory, drawn from Shannon’s Mathematical Theory of Communication, VE captures levels of CMC unevenness and group structure and visualizes them on wiki Web pages through background colors, charts, and tabular data. Visual information provides users entropic feedback on how balanced and equitable collaboration is within their online group are, while helping them to maintain it within optimal levels. Finally, we present the theoretical and practical implications of VE and the measures behind it, as well as illustrate VE’s capabilities by describing a quasi-experimental teaching activity (use scenario) in tandem with a detailed discussion of theoretical justification, methodological underpinning, and technological capabilities of the approach.

Robert Bruno
Doctoral Dissertation, 2010

This dissertation examines social self-structuring processes and group collaboration online with a special focus on their learning effects. Review of empirical and theoretical literature suggests that functional differentiation and participation inequalities are a constant phenomenon in small and large groups, both online and off. This dissertation examines what effects these inequalities may have on group performance, especially with respect to learning. An optimal unevenness range is proposed in the construct model wherein a curvilinear relationship exists between participation evenness and learning outcomes. A key element of Shannon’s mathematical theory of communication, the entropy function, is proposed as a measure of interaction in a wiki online collaborative environment. The author’s hypothesis of a curvilinear relationship between relative group participation and learning gain is informed by a broad range of literature from small group communication, system-level analyses, recent examinations of mass collaboration and open source software development, collaborative learning theory, developmental psychology, visual feedback technology, and information theory. A quasi-experimental design was proposed as the best way to test the hypothesis and answer relevant research questions. The procedure included 170 participants on teams of 4-11 co-constructing Purdue-related topics within three broad groupings, 1) a regular wiki, 2) the entropy-enabled Visible Effort Wiki, and 3) offline. Variant entropy levels and control variables were correlated with learning gain, as measured by pretests and posttests. Perceived measures were added as well. Research results and data analysis showed the overall model to be statistically significant at the group level. All predictor variables, including entropy, technical competence, and effort level, and excepting knowledge of Purdue University, were significant. On the individual, perceived level the overall model showed a significant linear effect and all predictor variables, except knowledge of Purdue, were significant. Results support a curvilinear relationship between participation levels and learning for objective measures, and overall findings showed a clear relationship between these two variables.

Sorin Adam Matei

Sorin Adam Matei - Professor of Communication at Purdue University - studies the relationship between information technology and social groups. He published papers and articles in Journal of Communication, Communication Research, Information Society, and Foreign Policy. He is the author or co-editor of several books. The most recent is Structural differentation in social media. He also co-edited Ethical Reasoning in Big Data,Transparency in social media and Roles, Trust, and Reputation in Social Media Knowledge Markets: Theory and Methods (Computational Social Sciences) , all three the product of the NSF funded KredibleNet project. Dr. Matei's teaching portfolio includes online interaction, and online community analytics and development classes. His teaching makes use of a number of software platforms he has codeveloped, such as Visible Effort . Dr. Matei is also known for his media work. He is a former BBC World Service journalist whose contributions have been published in Esquire and several leading Romanian newspapers. In Romania, he is known for his books Boierii Mintii (The Mind Boyars), Idolii forului (Idols of the forum), and Idei de schimb (Spare ideas).

18 thoughts on “Can social entropy theory explain social media?

  • November 7, 2010 at 5:25 pm

    Social entropy is a measure of social disorder. Social entropy is maximized when group members are all equally likely to communicate and share collaborative efforts on the same level. We can’t easily predict who is going to be next to contribute some content when all members are equally likely to do so, resulting in a natural state of disorder. Entropy levels of social situations captures how much structure is in a given group, which becomes proportional to entropy itself.

    Consider the following scenario (which could, theoretically, exist as online collaboration): I want to work on a manuscript with two others. I meet with those collaborators to work on a manuscript. Collaboration will likely not be equal here. Sometimes it can be valuable for one person to take on more work than the others, especially if that person has the primary idea they want to develop, and the support of others’ contributions and ideas can work together towards a cohesive goal in that manuscript. At the same time, that’s not to say that collaboration should always be in favor of one individual’s contributions over another. For instance, say that there are other results coming out of a data set that another person on that research team may wish to explore. They may take the lead on a future manuscript, and their collaborators may take on a role with less contributions, but still make valuable insights that would help to enrich that scholarship.

    It isn’t that total equity cannot be attained, but perhaps inequity is desirable. This comes out of social entropy, because we shouldn’t necessarily strive for contribution equity.

    Can social entropy explain social media usage? For some communication systems, yes—and for others, that remains unanswered. Within Wikipedia, for instance, research has examined social entropy as a measure to understand the system’s diversity and participation. Wikipedia research in particular is a prime example of a system where the majority of contributions are dominated by a few individuals, rather than many.

    Social entropy may also explain usage on Facebook, because it supports a self-serving role or motivation. An individual that is motivated to participate on Facebook may be so in order to post relatively equal contributions as other members in order to gain status on that web site. That site favors individual needs, rather than contributing towards more global, information-seeking needs (arguably, Wikipedia). If our contributions are due to random chance, then what might motivate me to contribute more is a gain in power and status in the community for contributing more, rather than someone else who may be equally as likely as I am to post something.

    There’s something to be gained from collaboration on social media web sites. The top contributors to Wikipedia, for instance, have gained notoriety for large contributions. Power and status are obvious examples of gain, but there may be others as well. Certainly, social entropy can help to explain such behaviors, in addition to other theoretically grounded examples that can lead us towards better understanding participation and motivating factors in such communities.

    • November 9, 2010 at 1:09 pm

      Excellent comments Becky… Linking motivation to entropy is an interesting idea.

  • November 8, 2010 at 1:37 am

    According to Shannon and Weaver (1949) in thermodynamics entropy is described as the “quantity which uniquely meets the natural requirements that one set up for information”. In the physical science entropy is viewed “as a measure of information”. The entropy that is associated with a particular situation may be describes as a measure of the level of randomness in that particular situation. Entropy is also related to physical systems and their tendency to become less structured or organized. The application of entropy to communication theory may also be described based on the construction of messages and the amount of freedom in deciding how these messages are constructed (Shannon and Weaver 1949). In applying the principles of social entropy theory to communication which is based on the idea that communication may be considered in ways that are related to a physical system (Shannon and Weaver 1949).

    Shannon Social entropy theory not only applies to communication but also social interaction. Social interaction is a major component of communication which involves various exchanges among humans. Social interaction may be viewed as an extended process of communication which depends on a system of symbols. In a social system where people interact with one another on a random basis that is free, such interactions may lack any identified structures. However, in social systems where interaction is more structured or organized symbols may be exchanged based on specific rules. In social interaction that is random with less organized structure the various exchange among individuals is more likely to be even which means that all individuals will exchange information or symbols with one another while in a more structured and organized social system there may be some level of bias as it relates to the sending of information by various individuals within that system (Matei, 2010).

    According to Matei (2010) from a statistical point a view social entropy measures the degree to which certain units or individuals of a system are more likely in their contribution to the function of that system as oppose to what could be predicted by chance. The likelihood of a member of a group or a particular social system to participate in communication and contribute or support that group as other members do result in increase social entropy. Statistically, the contribution of each member of the social system or group will not be higher than what could be predicted by chance. Therefore, the interaction would be merely random. However, if individuals contribute and communicate in an organize or structured manner by interacting with other individuals in the system the level of contribution is not random. Such outcomes cannot be predicted just by chance. Entropy will begin to decrease when it is measured based on the likelihood of random contribution of individuals within the social system. When individuals within the social system display non random behaviors or contribution this result is more than just unevenness and variation from what could be predicted by chance. The manner in which individuals interact among themselves and the roles they play as well as the rules that govern these interactions are interconnected (Matei, 2010).

  • November 8, 2010 at 2:55 pm

    With an understanding from the readings that online collaboration takes on many of the characteristics of off-line interaction (Matei and Ball-Rokeach 2001) and works best when the collaborative effort is uneven (Barabasi 2003; Huberman 2001) – with less people doing more of the work and having more of a say (Kittur et al. 2007) – how then do online and offline collaboration differ?

    The claim that open communication systems should strive for diversity and equality must be examined further to understand whether equality and diversity are actually “functionally desirable,” and whether the diminishing level of social entropy (as groups get established) serves to make the online groups more rather than less productive (Matei, Oh & Bruno).

    Unlike those who subscribe to Utopian ideals, I believe that a life is — on a macro-level — a linear process with very defined and bounded opportunities for substantial change of circumstances, advancement, and/or personal growth. In my view, the groups, careers, organizations, habits, skills, patterns of doing things, etc., are begun earlier in life, and the effort to make change later in one’s life (i.e. to join a new club, to learn a new skill, to change jobs, to get involved in a new hobby) requires exponentially greater effort and resources.

    While the power ratio associated with the online and offline worlds seem to mirror each other, the online experience, in my opinion, seems to favor different players because it offers a low threshold for entry compared to the real world complete with its numerous social, cultural, political barriers (of age, experience, skill, etc.). Researchers found that the more one participates in a collaborative group, the more one learns about its social structure (Bruno, 2010), and the more likely one is to be perceived as a leader (Bales, 1950). Once through the virtual door, individuals who contribute more to the conversation can rise through the ranks to be part of the group’s functional hierarchy. So, it seems, the gold star doesn’t automatically go to the privileged.

    Online, leadership later in life may really be possible. Your rise to the “top of the heap” there doesn’t seem to be as crippled by other people’s biases or your own social insecurities. You contribute because you are motivated to do so…period. In the public forums, you don’t have to know somebody that knows somebody. Your expertise and/or way with people combined with your ability to learn the rules and norms of the site will take you as far as you want to go. But you’ll have to get there on your own steam. The reward for your contribution (in a quantitative sense) does nicely illustrate the oft-mentioned moral advice: “you get out of it what you put into it.” And you don’t have to make a lifetime commitment to your role, either.

    What motivates people to contribute and “take” ownership of a site online is for another discussion. But the fact that they can and do contribute; that their contributions, many times, are valued; and that no one particularly cares whether they’re living a trailer is a very beautiful thing.

    I’m beginning to buy into the thought that an online environment does not provide the kind of democracy whereby everybody has an equal say…but it does have the capability of fulfilling the “pursuit of happiness” aspect of the democratic mindset. Barriers are lowered to participation and real involvement/power, although not guaranteed, is a step closer online than off.

    • November 9, 2010 at 1:05 pm

      Your point that unevenness of contribution is the product of limited bandwidth and time budgets is excellent. As for what kind of democracy we want online, that should be one based on equality of chances, not of results. Yet, to attain this ideal much more needs to be done, as we do not have a clear picture of the opportunities and of the people situated in the situation to take advantage of these opportunities. We emphasize a lot the material-technical digital divide, but we do not deal adequately with the social-institutional divide that opposes those who are in the know, who are at the levers of the digital economy, not only as producers, but also as prosumers. Often they use their first mover advantage to consolidate their power and keep the “masses” at bay. This is what happened, for example, in Second Life, where what amounts to a mafia took over the thriving commerce of that place. A lot of the top Ebay sellers, Wikipedia contributors, YouTube posters and Facebook “superfriends” got there by knowing better and ahead of the crowds the inner workings of these environments. We need to reveal to the world both how this knowledge is to be acquired and who the people who got access to it are.

  • November 26, 2012 at 3:06 am

    Social entropy deals with the amount of diversity that exists in contributors to a project. Applied to social media and Web 2.0 content, such as Wikipedia, this idea of entropy calls one to consider whether or not one person is responsible for a majority of the content, or if the content has been created equally by all contributors. This “social entropy” is at its highest when all members are contributing equally leading to a disorder and unpredictable nature of the project ( Because information is coming equally from so many different angles, it is unsure who will contribute next. This is contrasted with a project of low social entropy in which one contributor is making up a majority of the content. The key question that stems from applying this idea of entropy to social media is, “how much entropy is ideal?”
    Generally speaking, entropy is considered a good thing in group work. This can be seen in the push for diversity in the professional world, as many managers assume that the more different contributors are, the more variety of ideas they will produce, thus their chance of coming up with a great idea is increased. However, is this the case with social media? The article “Collaboration and Communication in Online Environments: A Social Entropy Approach” by Dr. Sorin Matei, et al. takes a look at the online collaborative encyclopedia, Wikipedia, and seeks to determine how social entropy studies can be applied to online platforms to study how much diversity exists in a given piece of content and then brings up the need to answer whether or not the presence of entropy is beneficial in online communities ( The findings from the Wikipedia case study revealed that as time increases, the entropy number nears 1, the maximum. This shows that Wikipedia articles tend to gather more contributors and become a more diversified collaborative effort over time. However, as we discussed in class last week, a majority of Wikipedia users do not contribute to the site and from those that do contribute, almost all of the content is created by a select few top contributors. This questions that while entropy does exist, is the way that content on the site is produced really as unpredictable as thought? Additionally, while entropy does exist as a result of the number of diverse contributors, this entropy makes it difficult to ensure that all contributors are reliable.
    This idea of entropy and diversity on social media should be considered with Twitter in mind. The content that exists on Twitter is updated constantly by the estimated 250 million active users on the site ( However, while so many millions of people contribute, how many people are actually able to spread their ideas to the masses? The way Twitter exists (reliance on retweeting) caters to the ability of crowdsourced elites to rise relatively easily (Papacharissi & Oliveira, 2012). Even though content on Twitter is generally considered to be something that is created by many, only the few actually create content with any significant reach. While the potential for disorder and unpredictability exists, the fact that only a few people have the reach necessary limits the effect of entropy on Twitter. Similarly, the filter bubble that exists resulting from users tendencies to pull information from only sources that interests would seem to keep the entropy number of an individual’s content low.
    This varies from Wikipedia in that while only a select few on both platforms may actually shape content on a wide scale, Wikipedia allows almost anyone to have that ability. If I were to log onto Twitter right now and post “The new Lord of the Rings movie comes out on December 25th (not accurate)”, it would only have the power to reach my several hundred followers. Even if it was retweeted by a friend, it is unlikely the retweeter would have many more followers. However, if I were to go onto Wikipedia and change this information, it would be available for the whole world to see until someone corrected the incorrect date. It is this factor that causes Wikipedia to have a much higher level of entropy than other social media platforms like Twitter and Facebook, individual users are able to easily create content easily available on a much larger scale.
    The amount of entropy that exists on various social media is important to understand for several reasons. First, one must understand so that they know how diverse the sources are behind the content. The diversity that exists on Wikipedia allows for a less biased nature of content than tools like Twitter which have less diversity of opinion within their sources. Another reason that it is important to understand how much entropy exists, deals with reliability of content. While the disorder on Wikipedia has many benefits, this very aspect is a contributing factor as to why it is not considered a reliable enough source to cite in academic papers. In order to have such a diverse collaborative effort, it would be unfeasible and inefficient to heavily regulate who is allowed to produce content and what they are allowed to contribute. This entropy will continue to be a factor on the reliability of social media as the sheer number of contributors to content, with no enforced guidelines or strict code of ethics, continue to bring down the credibility of social media as a whole. In order to produce the best content on social media as possible, a middle point must be met in which the diversity of contributors is maximized but the content produced can still be regulated to an extent. Until this happens, social media will continue to be a tool that can never be guaranteed reliable.

    Papacharissi, Z., & Oliveira, M. F. (2012). Affecting news and networked publics: The
    rhythms of news storytelling on #Egypt. Journal of Communication, 62(2), 266-282. DOI: 10.1111/j.1460-2466.2012.01630.x

  • November 26, 2012 at 12:10 pm

    Shannon and Weaver in 1948 proposed the idea of social entropy theory and how it applied to information. It stated that the lower the level of randomness and the higher that of order, the more likely that a communication act carried meaning.

    Matei et al. used social entropy theory to approach the measure of diversity in online communication spaces. Theoretically, in online spaces, we experience high entropy – that is to say there is high diversity and thus more diverse participation. Matei et al proposes social entropy theory as a way to measure, and thus later manipulate levels of group collaboration and contributions.

    It is an interesting notion to consider, especially in terms of optimization of entropy for maximum effectiveness of collaboration. While trying to maximize collaboration might work well in situations where abilities and levels of contribution can be controlled for (eg in offline small groups and organisations), this might be significantly more difficult in online collaboration, where there is potential that we do not know who exactly we are working with, such as on online forums, or other social networking groups.

    We also need to consider that equity might not be the most ideal situation for many collaborative efforts. For instance, a collaborator might have more vested interest in a situation and thus have more relevant contributions to it, rather than another who can only make shallow contributions. In other situations, it might be necessary for a person to take the lead in order to guide a collaborative effort.

  • November 26, 2012 at 12:25 pm

    To answer the question posed by this module: I say yes. Social entropy theory provides us with a mathematical mechanism for understanding how it is communicative systems work – i.e. the actuality of their present and former states as well as the potentialities available to them. It seems to me, though I could easily be wrong because my math is woefully inadequate, that there is still some work to be done to hash out the mathematical formulae, but regardless I believe an appropriate reaction to an entropic model based on the second law of thermodynamics is, as Weaver put it, “when one meets the concept of entropy in communication theory, he has a right to be rather excited— a right to suspect that one has hold of something that may turn out to be basic and important.” (Weaver, 1949).

    That being said, we must remember the limitations of social entropy as a concept, and consider some of the differences between describing the physical laws of nature and describing the social realities of communicative systems. For example, when one reads about the laws of thermodynamics there is always an assumption that there is such a thing as “states of equilibrium.” That is to say, it is possible for physical systems to be in such a state. I think we could plausibly infer that no such state could exist for social systems, but the question then becomes whether or not with this difference, we also have distinction.

    I would propose that there is, and that this is not only problem for social scientists but is increasingly becoming one for physical scientists as well. Although physicists have long been invested in discovering the fundamental “laws” of nature that apply in all cases at all times, since the rise of quantum mechanics there has been an increasingly frustrating elusiveness of any such principles. Physicists find that there may be very different rules at small and large scales than there are for so-called “medium-sized” objects. Seeing this frustration for those studying so staid a field as the ever-ordered natural world, we who study the social realm should be extremely reluctant to fall into the same trap of applying “laws” that exist independent of their circumstances especially considering the fierce unpredictability of systems with a social component (Taleb, 2007). Thus, we must not confuse “basic and important” with “universal and abiding.”

    That aside, as I said before I do think mathematical models of social entropy illuminate and in many ways explain social media. Matei is right to point out that failing to study social entropy itself ignores, “a gauge to directly measure and assess these levels of collaboration and their significance.” (Matei, 2005). We must remember, however, that though these numbers may significantly help us paint a picture of what we know to be going on in a given social system, we never have access to all the colors we need.

    Matei, Sorin et al. Collaboration and Communication in Online Environments: A Social Entropy Approach. Paper presented at the NCA Annual Conference, San Antonio, Texas, November, 2005

    Taleb, Nassim. The Black Swan, 2007, New York, Random House.

    Shannon and Weaver, Mathematical Theory of Communication, 1949, Urbana, University of Illinois Press.

    • December 5, 2012 at 2:30 pm

      Excellent thoughts, but one repartee is needed. Entropy is to be applied to social systems not to show that they are or should exist in a state of equilibrium. To the contrary, it is to be used to detect when the system is not even, not harmonious, not “naturally settled.” Also, entropy is to be used as social measure, as a thermometer, not as an over encompassing theory.

  • November 28, 2013 at 8:08 am

    Entropy is a term used across the sciences, reaching back to at least the mid-nineteenth century (Bailey, 1997). In the Mathematical Theory of Communication, Shannon and Weaver (1949) use “social entropy” as a measure of the relative state and structure of an information system. More specifically, “entropy” provides an indicator of “social disorder,” also defined as “uncertainty” or “diversity” (Bailey, 1997; Matei, Oh, & Bruno, 2005). Social entropy is high when all group members are likely to communicate or contribute at the same level (i.e. more egalitarian groups) and low when there is an imbalance (Matei, Bruno, Faiola, & Morris, 2010). Groups with more equality and diversity in contributions are more random and difficult to predict and less structured than those with low diversity. Structure and social entropy are then, according to Matei et al. (2010), inversely proportional.

    Social entropy can be applicable to social media environments, particularly as scholars, as Matei et al. (2005) do, seek to examine claims that online spaces are more egalitarian, diverse, and heterogeneous. Despite such claims, many inequalities exist in online participation. As Matei et al. (2010) note, 10 percent of Wikipedia editors contribute nearly 90 percent of the website’s articles (Ortega et al., 2008 as cited in Matei et al., 2010). How this happens, when it takes place and why, and who benefits are all pertinent questions for scholars to address and social entropy can be one measure to employ. For example, as sites like Facebook and Twitter gain more and more users, and arguably more diversity, the question then becomes does this lead to equality in participation or do certain voices dominate? At what point do contributions to an information system (represented either in an individual’s network or the larger site as a whole) become structured and to what degree? And what are the implications?

    Of particular interest is the idea of what the “optimal” level of entropy is for a given system (Matei et al., 2005). High levels of entropy may be difficult to manage and maintain, requiring more time and resources. Over time this could lead certain individuals to leave or pull back their contributions, others to become the “key” contributors, and hierarchies to be formed. Low levels of entropy mean a loss in diverse opinions and views, resulting, in just a few voices dominating the discussion.

    Social entropy can then be seen as useful for examining the state of a social media site at a given point in time but also the life cycle of the site and its structure. It seems it may also be a helpful measurement tool for practitioners who want to maximize diversity but also engage in effective and efficient collaboration within the system.


    Bailey, K. D. (1997). System entropy analysis. Kybernetes, 26(6/7), 674–688.

    Matei, S. A., Bruno, R. J., Faiola, A., & Morris, P. L. (2010). Visible effort : A social entropy methodology for managing computer-mediated collaborative learning. Paper presented at the Global Communication Forum, Jiao Tong University, Shanghai.

    Matei, S., Oh, K., & Bruno, R. (2005). Collaboration and communication in online environments: A social entropy approach. Paper presented at the NCA Annual Conference, San Antonio, TX.

    Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication. Champaign, IL: University of Illinois Press.

  • November 29, 2013 at 9:48 am

    The mathematical model of communication by Shannon and Weaver proposes that a typical communication model comprises of an information source, a transmitter, a receiver and a destination. The information source in this model has an unlimited number of messages that it can choose from, and within this theory, information refers to the ability of the source to make a choice or select a message from that unlimited number.

    Moreover, the theory proposes that after the information source selects a message or a number of messages from the infinite choice available to it, the transmitter encodes this message into a signal that it then transmits via the appropriate communication channel, to the receiver. The receiver in turn, decodes this message and delivers it to its destination and the destination, acts upon that message. However, the mathematical theory suggests that during this process the channel might experience noise, or a source of distortion to the message. Noise in a communication channel is an error that can decrease the accuracy of the message obtained by the receiver and as a result, can affect the ability of the destination to understand the message and act appropriately upon it.

    Furthermore, according to the mathematical theory of communication, the more information communicated via the channel, the higher the possibility that the communicated message will incorporate error and, the greater the uncertainty regarding the accuracy of the message received by the destination. Therefore, the mathematical information theory of communication is concerned with reducing noise in a communication channel and uncertainty and as such, presents a mathematical model by which it can estimate the probability that a channel can accurately transmit a message given the expected amount of noise, uncertainty and the information it incorporates. It also suggests that by using the appropriate coding method pertaining to a channel, it can maximize the rate by which useful information is transmitted to its destination (Weaver, 1949: 12).

    Another aspect of this theory is related to the issue of redundancy. In general, the mathematical theory of communication suggests that every instance of message communication typically incorporates a certain level of unwarranted redundancy. According to Weaver (1949), redundancy represents the fraction of the message communicated via the channel that is not freely selected by the information source but that is nevertheless, incorporated into the message because of the “statistical rules” (Weaver, 1949: 7) that require its addition to certain symbols. According to this theory, reducing redundancy in a communication channel can increase the overall accuracy of the message received by the intended destination however, in certain circumstances, maintaining redundancy is advantageous because it can also act as a resisting mechanism to uncertainty and error.

    One of the central concepts in the mathematical theory of communication is entropy, which refers to the level of randomness, disorder and disorganization in a communication channel. It is also a measure by which this theory can account for the diversity and the level of information transmitted within a message through the communication channel. This application of entropy has been extended to address macro-societal interactions within individuals in organizations, social groups and networks both online and offline. Social entropy suggests that overtime, organizations and social groups will inevitably experience an increase in its level of diversity, randomness and deterioration. As a measure, social entropy has been used to gauge the structural level of organizations and the degree of diversity between members’ contributions to these organizations.

    For example, Bruno (2010) utilized social entropy to measure the level of participation, in terms of inequality and evenness, in social groups in order to test a hypothesis that proposes, “there is an optimal level of participation inequality in relation to a performance outcome” (Bruno, 2010: 17). Likewise, Matei et al. (2006) used social entropy as a means to measure the level of diversity and inequality of contributions to the open online forum Wikipedia. By tracking the content added to a specific Wikipedia page by a number of contributors over the course of 8 months, Matei et al. (2006) was able to map the changes in the levels of contribution by a known number of participants and the levels of social entropy associated with these contributions. In this study Matei et al. (2006) demonstrated that the increase in the number of contributors and their level of involvement in this Wikipedia page was accompanied by an increase in the levels of social entropy in both the absolute and normalized terms. The normalized levels of social entropy were used by Matei et al. (2006) to reveal the unevenness in the level of participation between the same individuals involved in adding content to this particular page. What is more, from the results, this study predicted that normalized social entropy levels would continue to increase as more individuals contribute to this page however, beyond a certain point of informational saturation, the magnitude of the effect that the diversity of contributors has on the level of normalized social entropy will decrease and plateau.

    Furthermore, by revealing that diversity does not necessarily mean equality of involvement, this study challenged the previously wide held and simplistic notion that the Internet and the virtual social communities or open platforms it offers will foster a dialogue that is contributed to equally by all individuals. By using social entropy as a measurement of the inequality of contributions between members of a social forum then, this study revealed that in reality, there is a discrepancy and unevenness between the levels of involvement by individuals online concluding that the notion of the Internet as a place in which all members contribute to equally is incorrect.

    To conclude, social entropy theory and measurement is interesting because of its applicability to a number of different social phenomena including democracy. Social entropy for example, can be used to measure how democratic a country or a system really is and can be utilized to measure whether the people’s representatives in a democratic government for example, are contributing to the formation of policies at the same level? It can also help illuminate whether the level of contribution and power is equally shared within different governmental organizations in a democratic government for example, or if power is in reality concentrated within a few influential governmental organizations or even, within the hands of a few people? Moreover, it can potentially be used to estimate the inequality in the levels of influence and contribution between different non-governmental, political organizations and lobbyists for example, to politics and policies in a government.


    Matei, S.A, Oh, K, Bruno, R. (2006). Collaboration in Online Environments: A social Entropy Approach.

    Weaver, W. (1949). Recent Contributions to the Mathematical Theory of Communication.

    Bruno, R. (2010). A Democracy of Unequals: Social Differentiation, Participation Inequality, and the Collaborative Ideal Online.

    Information Theories. (Shannon & Weaver Model of Communication) University of TWENTE. Retrieved from

    Hicks, A. (1999). Social Democracy and Welfare Capitalism: A century of Income Security Politics. Cornell University Press.

  • November 29, 2013 at 12:07 pm

    Shannon and Weaver’s (1949) social entropy theory can help explain information-sharing within social systems. Social entropy is a measure of disorder in a social system. When all members of a social system are likely to contribute equally, the entropy of the system is at its peak. This is because it is difficult to predict who will contribute information next when there all members are possible contributors; there is no specific order or hierarchy to guide predictions. It cannot be predicted that contributions will be equal, either. Social entropy assumes that systems tend toward disorder; so, even when all members are equally likely to contribute, it does not mean they will actually contribute information equally. This concept can be observed in information-sharing behavior on social networking sites (SNS). For example, while people generally have equal access to SNS like Facebook and have equal opportunities to share information on the site, Facebook users do not contribute information (share content) equally. Depending on the information-sharing situation on Facebook, users vary on the amount of information they share within the system. In one situation, User A may collaborate with User B to engage in a problem-solving conversation, while User C simply observes (e.g. two people deciding what restaurant to choose on a message thread regarding a group outing). In a later situation, the Users A and B may choose not to contribute, while User C emerges to contribute information (e.g. one person sharing an article he or she found interesting with the same group).

    Sorin et. al. (2005) argue that concepts like social entropy “can help us express the amount (quantity) of collaboration in online environments in such a manner that collaborative process can be compared across groups, settings, and time periods.” Social entropy theory could help explain contribution within specific SNS like Facebook. (For example, how do people collaborate on the site and how does this explain changes in the amount of information users share?) This theory could be especially helpful in examining the differences in collaboration across different SNS. How do people share information and collaborate on Facebook versus Twitter, etc? What do these differences tell us about the system and about the needs it may satisfy for those who contribute information within the system? Exploring questions like this using social entropy theory could help us understand SNS as information systems and could highlight what makes each system unique.

    Matei, Sorin et al. Collaboration and Communication in Online Environments: A Social Entropy Approach. Paper presented at the NCA Annual Conference, San Antonio, Texas, November, 2005

    Shannon and Weaver, Mathematical Theory of Communication, 1949, Urbana, University of Illinois Press.

  • November 24, 2014 at 12:33 pm

    Regarding the question of whether social entropy theory can explain social media, I think the answer is no. Information theory was a descriptive theory and was not made to be used in a predictive or explanatory manner (Osgood & Wilson 1960). Social entropy theory, which originated out of information theory, has not diverted from this general purpose. Although it is more sophisticated and was formulated to directly apply to communication contexts, it remains primarily as a tool of measurement (Matei 2010). Specifically it measures variations and differences in individual contributions within a group system.

    Even putting semantics aside, I am still unsure that the answer could be yes. It is obvious that social entropy theory is extremely useful to studying social media, from measuring the diversity in online contributions (Matei et al., 2005), to tracking that diversity and finding patterns (Bruno 2010). It is stated again and again that social entropy should not be an end in and of itself (Matei et al., 2005). Instead it should be used as a tool to further theoretically based inquiry regarding communication systems. I am not so sure that tools are explanatory.

    Finally, my main question in response to the module is, explain what exactly? It is a pretty big task to explain all of social media. Additionally, what other competing explanations are out there that could explain social media better? If it were to be decided that social entropy theory explained social media, I think it would be preferable to narrow that explanation to make room for other processes that may not be as well explained by social entropy theory.

    Osgood, C. E., & Wilson, K. (1960). Some terms and associated measures for talking about human communication. Institute of Communications Research. Mimeo.

    Matei, S. (2010). Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated Collaborative Learning. URL:

    Bruno, R. (2010). A Democracy of Unequals: Social Differentiation, Participation Inequality, and the Collaborative Ideal Online. URL:

    Matei, S. et al. (2005). Collaboration and Communication in Online Environments: A Social Entropy Approach. Paper presented at the NCA Annual Conference, San Antonio, Texas.

  • December 1, 2014 at 6:33 am

    Social entropy is a fascinating concept as applied to the world of social media and mass communication. I am always curious as to how someone can take a theory from a distinctly different discipline and figure out how to apply the principles to another discipline, all while trying not to discombobulate the original principles. Social entropy is based on entropy as related to the principals of decay in thermodynamics. According to Palomino (2002), social entropy is defined by applying the second law of thermodynamics to human social behavior. Palomino (2002) goes on to state that there must be an assumption the social entropy is equal to the degree of social dissatisfaction with in a given social system. The amount of social entropy at a point in time is measured utilizing a Boltzmann equation and solving or simplifying the equation with a Stirling formula. The end game of this complicated and unique marriage of disciplines, is to show social entropy increases with time similarly to the degree of disorder in a thermodynamic system increases with time (Palomino, 2002).

    I have to admit that I was perplexed at how this marriage of disciplines, physics and human behavior, was going to work, especially with my math skills more suited for simple addition and subtraction than for understanding thermodynamics! However, Palomino’s (2002, p) example utilizing the “alien visitor” model helped me achieve a basic understanding:

    “An alien visitor is outside our atmosphere and is observing human behavior, but the alien is invisible to humans on earth. Our alien is able to distinguish the individual movements of humans. The alien would quickly discern that chaotic rules apply to human movement. Applying the second law of dynamics to human movement; the alien would see that humans display a lot of irrational behaviors, like war and riots. The alien would then ask, what motivates such human behavior” (Palomino, 2002, npn)
    “Suppose now that this alien visitor gets closer to the earth (remember, he is invisible to human beings) and manages to learn the reason why the human beings behave that way. Soon he would be able to understand that such apparently unusual behavior is consistently motivated by a lack of some degree of freedom; which may be summed up as a state of satisfaction or dissatisfaction. Viewed in this way, our social system may be approached through the second law of thermodynamics. (Palomino, 2002, npn)”

    This example of alien human watching, helped me understand the relationship between the entropy and social entropy in regards to thermodynamics. Not being a physicist, I had to do research on my own to help understand the processes involved and how the two puzzle pieces might fit together. I am still a little confused about the whole concept and maybe someone can reply to this and help me understand a little more.

    Palomino, A. (2002). Social Entropy: A Paradigmatic Approach of the Second Law of Thermodynamics to an Unusual Domain. Nexial Institute, Dallas, TX.

  • December 1, 2014 at 10:41 am

    In the social media age, Facebook, Twitter, and other social media platforms offer opportunities for people to express their own opinions. Knowledge sharing on social media is a collective wisdom process. The more individuals engage in social networks, the more wisdom they create. In the book The Wisdom of Crowds, Surowiecki (2005) states that it is possible to aggregate information from people all over the world and arrive at a collective decision. The phenomenon of the wisdom of crowds changes the nature of collaboration. Lévy, (1999) in his book Collective Intellect argues that the new media environment provides a new “knowledge space,” and that it is being transformed by the existing structures of knowledge and power. He argues that new technology promotes online communication, increases civic participation in decision-making, promotes an interactive information flow and minimizes constraints on communication. Online groups generate collective intellect and debate meanings and interpretations for contemporary culture. Furthermore, the information flow is not unidirectional but becomes very multidirectional. Information flow is no longer a “two-step flow” but a “multiple-step flow”. How can we translate the higher level of disorder to useful information? How can we quantify the level of online involvement? How can we know whether diversity lead to cooperative acts or chaos? Social entropy approach provides a sophisticated way to measure the equality of contributions, community dynamics, and diversity of social and communicative systems.
    Shannon and Weaver (1998) formulates that entropy is a measurement of information diversity in the system. If the information redundancy is lower and the order is higher, the communicative act would be recognized as information rather than noise. It means that the various elements in communication system have an equal proportion showing the greatest level of diversity. If the proportion of elements is imbalanced, the entropy is low. This egalitarianism of online collaboration can recreate social order and collaboration (Johnson, 2001) and more likely to generate useful solutions (Raymond, 2001). Social entropy index provides a tool to directly measure the levels of collaboration and their significance (Matei et al, 2005), and it is also a useful way to observe the dynamics in online communication. Increasing numbers of people are reaching out for knowledge or solutions to problems on social media rather than using professionals. They are finding practical solutions and then giving feedback to build up organization in online community. This user-generated content process emerging in many online communities. In social media, a significant body of information and opinions are aggregated by the online community, which provide insight into people’s attitudes. So we need to have a better understanding of the formation and impact of information diversity and its’ impact on social media.
    Johnson, S. (2001). Emergence: The connected lives of ants, brains, cities, and software. New York: Scribner.
    Lévy, P., & Bonomo, R. (1999). Collective intelligence: Mankind’s emerging world in cyberspace. Perseus Publishing.
    Matei, Sorin et al. Collaboration and Communication in Online Environments: A Social Entropy Approach. Paper presented at the NCA Annual Conference, San Antonio, Texas, November, 2005
    Raymond, E. S. (2001). The cathedral and the bazaar: Musings on Linux and Open Source by an accidental revolutionary (Rev.). Cambridge, MA: O’Reilly. Shannon, C. E., & Weaver, W. (1998). The mathematical theory of communication. Urbana: University of Illinois Press.
    Surowiecki, J. (2005). The wisdom of crowds. Random House LLC.

  • December 1, 2014 at 11:54 am

    The social entropy theory developed from the “theory of communication” by Shannon and Weaver (1949) helps people understand and measure the disorder in a social system. In the theory of communication, researchers have proposed a communication system which can be represented as the information source, the transmitter and the receiver, in the transmitting process of which noise source could come into play (Shannon & Weaver, 1949). The degree of information (or the entropy) is associated with two factors, the organization and the redundancy. The more highly organized (the less randomness of choice), the lower the entropy is. In addition, the concept of entropy is also borrowed into the discussion of diversity. Researchers studying the online collaboration have found that the social entropy theory is very helpful in measuring the degree of diversity and equality objectively in the context of a virtual environment. Matei, Oh and Bruno (2005) have reviewed that “When the elements are present in an equal proportion, thus present a maximum level of diversity, the system is said to have a high level of entropy. When the elements present a level of imbalance, entropy and diversity are low” (p. 5). In this respect, the level of social entropy reaches its maximized level when all elements present the equality and balance.

    However, the concept of “equality” is very subtle and can be further explored. When thinking about having equality, particularly in the online environment, it is interesting to question what aspect of equality it wants to refer to or highlight. Borrowing the first digital divide and second digital divide discussion into the discussion here, the “first” level of equality could refer to the physical access equality to the Internet and the virtual world. The “second” level of equality will lay in the field of actual “usage” of the Internet. With respects to the social entropy theory, the first level of equality seems to be widely acknowledged that this physical access equality is achieved that the access to the Internet does not display any hierarchy. The confusion lies in the second level in terms of people’s actual use of the Internet. When talking about equality and balance in the social entropy theory, it is hard to relate to the “equality”. From daily experiences, it is clear that not all people use the Internet in the same way. For example, on the social networking websites like Facebook, some users are apparently more active on these sites while some other users are nearly silent or invisible. The active users are likely to be producers of the online content, contributors to the online discussion and promoters of online information while the “invisible” users (like me) would only consume information, hardly contributing anything to this online platform. In considering this, it would be interesting to measure and compare the two groups and decide if they are equal in the online environment. Therefore, to explore the concept of “equality” in terms of social entropy in the online setting, it might have to start with the definition of “equality”, whether it being the equality of opportunity or the equality of contribution.

    The other interesting aspect to think about the diversity in the social entropy theory is to examine the influencing factors of the diversity in the online collaboration. As Matei, Oh and Bruno (2005) have proposed, “The diversity of opinion in communication spaces is a function of the number of participants and the shares of participants. More participants means more diverse participation – and the more uniformly distributed the contributions by member of a community imply more diverse participation” (p. 12). This is very helpful in helping studies objectively measure the degree of diversity. However, to continue the discussion of “equality” in reflecting upon the diversity here, it might be interesting to see if equality could determine the diversity. For example, in the theory of spiral of silence, dominant opinions are more present while non-mainstreaming opinions might be suppressed. This phenomenon can be true in the online collaboration environment as well. If the spiral of silence is displayed, the equality of participation is not guaranteed, thus the diversity of information can be reduced accordingly.


    Matei, S., Oh, K., & Bruno, R. (2005). Collaboration and communication in online environments: A social entropy approach. Paper presented at the NCA Annual Conference, San Antonio, TX.

    Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication. Champaign, IL: University of Illinois Press.

  • December 1, 2014 at 11:50 pm

    While the definition of communication remains as contested as its nature, claiming that communication requires at least two people in many ways proves axiomatic. While Weaver’s thoughts have provided a firm foundation for theorizing about communication for decades, his focus in his 1949 article was on the sending and receiving of messages, not necessarily the responding to them. Thus, it is not surprising that he claimed that entropy “is associated with the amount of freedom of choice we have in constructing messages” (Weaver, 1949, p. 6). However, to understand social entropy, one must examine not only the randomness of message production but also the randomness of message feedback.
    At its core, entropy concerns randomness. Matei, Bruno, Faiola, and Morris (2010) claimed that social entropy via social “is maximized when a group member is just as likely to communicate, share the effort or contribute an output unit as any other member.” From a regression perspective, the researcher cannot predict who is going to communicate. However, this conceptualization is limited when applied to feedback options. For example, if someone replies to my Facebook post with a direct question to me, I am more likely to respond than are any of my friends. The likelihood of my response is nonrandom as one could predict the chance of my response over the likelihood of a random friend’s response. Ironically, entropy becomes a more complicated process in the context of message response.
    Therefore, social entropy relates not only to the construction of messages but also the feedback to messages. For example, a Facebook post in which any of my friends are as likely to respond has a higher level of entropy than one in which certain of my friends are more likely to respond than others. Albeit this process is more complicated on non-social media outlets as the audience is less clear, but the claims of randomness still relate if users have the ability to create and reply to message.
    The question then becomes under what conditions is social entropy more likely to exist online than in other situations. Judee Burgoon’s interpersonal adaptation theory (IAT) can be expedient to answering this question. IAT claims that communicators enter a social episode with requirements, expectations, and desires, all three of which make one’s interaction position. Using the theory, people communicate only if they have an expectation, requirement, or desire in the episode. Thus, the contexts with the highest level of entropy are those that offer equal expectations, requirements, and/or desires to all communicators as they all are likely to give feedback. Although a detailed application of this theory is beyond the scope of this post, it provides a basis for theorizing more about entropy.

    Burgoon, J. K., Stern, L. A., & Dillman, L. (1995). Interpersonal adaptation: Dyadic interaction patterns. New york: Cambridge University Press.
    Matei, S. A., Bruno, R. J., Faiola, A., & Morris, P. L. (2010). Visible effort : A social entropy methodology for managing computer-mediated collaborative learning. Paper presented at the Global Communication Forum, Jiao Tong University, Shanghai.
    Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication. Champaign, IL: University of Illinois Press.

    • December 4, 2014 at 1:32 pm

      The feedback loop is at length discussed in the Osgood book. Also, message/information/communication exists when entropy is low (preferential connections exist), not vice-versa… Good thoughts, overall…


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