Category: Social networks

  • Small World Information Bubbles

    Small World Information Bubbles

    Small World Information Bubbles

    Information Bubbles

    We all live in an information bubble. Not because we are unaware of alternative perspectives, but because we prefer the perspectives of our “tribe” (or in-group).
    Fox News, CNN, and MSNBC are filling the trust hole left by eroding community life. Increasingly, extremist online media groups and politicized TV networks are exploiting the vacuum left by abolishing the fairness doctrine. There is no requirement for media sources to be balanced or objective in their presentation of news or facts [1].

    In the 1970s, 80s, and 90s research demonstrated that people only read newspapers that aligned with their political point-of-view (Knobloch-Westerwick et al, 2019). Now people seek out media, TV, and online news sources that align with their existing perspective – or are served with reinforcing points of view via social media filtering mechanisms.

    A filter bubble is the state of intellectual isolation that arises when personalized searches, recommendation systems, and algorithmic curation selectively presents information to each individual user (Pariser, 2011).

    Elfreda Chatman’s “Small World” Findings

    Elfreda Chatman (1991) showed how people in working class and marginalized communities prefer news from friends & neighbors to external sources. She described the world that less-educated or impoverished individuals inhabit, using six aspects of information-seeking. Chatman argues that poor and less-educated individuals tend to:

    1. Live life in a small world

    Information originating outside of their local circle of contacts holds little of interest for the lower class. Their information access is driven by the combination of living in a risky environment, life at the margin of influence and social participation, and “the awareness that if one wants acceptance, future goals and aspirations must be constrained by the standards of one’s family and friends.”

    2. Have lower expectations of success

    People in marginalized and poorer communities believe their success is governed by luck rather than opportunity or skill.

    3. Seek information only from direct or trusted contacts

    People (generally) prefer to seek information mainly from others like themselves, and are skeptical of claims not personally experienced. They view external perceptions about reality as not adequate, trustworthy, or reliable, which limits exposure to new possibilities or education.

    4. Have a limited-time horizon

    Their lifestyle is present rather than future focused. They base decisions on “the immediate present and the very recent past” rather than planning for the future.

    5. Have an insider’s worldview

    People in marginalized and poorer communities view the outside world as unpredictable and hostile. There is an “us vs. them” mentality, where people residing outside of one’s familiar surroundings are viewed with suspicion.

    6. Use the mass media differently than do higher socioeconomic classes.

    Marginalized people are heavy television viewers: “mass media, particularly television, is viewed as a medium of escape, stimulation, and fantasy” rather than an information source. They perceive news to be a reflection of events that occur locally and so they are more likely to be “mistrustful of others and afraid of being victims of crime.” They keep dogs and guns for protection.

    Chatman’s (1991) Small World theory has proved highly influential, as shown in Figure 1. This theory has been used to demonstrate how – because evaluating information in an online world is so complex – people tend to rely on members of their local community, or online influencers trusted by local community members, as sources of reliable information (Chowdhury & Chowdhury, 2013).

    Life in the round theory influence network

    Figure 2. Influence of Chatman’s “Small World” Information Theory
    (Gonza´lez-Teruel & Abad-Garcı´a, 2018)

    Chatman’s “small world” theory explains why Fox News is so subversive to society: it markets itself as the sole purveyor of truth and plays on distrust of people outside the group by pretending that their privileged journalists are just like “ordinary people.” Members of marginalized and poorer communities consume news as a medium of entertainment – they are relatively uneducated and can be indoctrinated without realizing it, as this Fox News presents perspectives from “people like us.” When trying to get a broadcast license in the UK, from where they were banned, Fox News described their content as entertainment, rather than news.

    Filter Bubbles in Online Communities

    Because social media and news media are driven by algorithm or network-connected interaction, they create a “small world” network for everyone, regardless of social class. On social media platforms, algorithms and the need to develop networks of regular social contacts can inadvertently isolate a user into an ideological filter bubble (Pariser, 2011), by only serving them information that it thinks they want to see. For example, Meta (Facebook, Instagram, and Threads) curate their posts to match them to posts on similar topics, or containing similar keywords and sentiment-related modifiers that users have sought out previously [2]. If you “like” posts from a particular perspective, those are all that you will see. Two examples of filtering mechanisms are:

    • On Threads, a Meta social media site which uses a preference-oriented algorithm to display posts for each user, there has been a lot of discussion about how the algorithm rewards people who “like” posts to sympathize with those whose dog or cat has just died, with a depressing, never-ending stream of posts about dead or dying pets.
    • On platforms with no filtering algorithm, such as Bluesky, the need to follow other users in order to obtain visibility and online-interaction imposes its own filter bubble, as people tend to follow those with similar perspectives to their own (people whose posts they enjoy reading).

    This creates an online small-world – an automated filter-bubble. Because of their limited, ideological information preferences, it is difficult to introduce people to alternative points of view. They see alternative ideological viewpoints – including factual support for counter-perspectives – as dishonest or subversive. When confronted by cognitive dissonance, they reframe the “facts” to fit with their beliefs, because of the importance of local community perspectives in their world. They engage in defense mechanisms such as avoidance, denial, or cherry-picking sources. Dissonance research has demonstrated that people are more willing to examine materials that confirm their beliefs than materials that dispute their beliefs. reinforcing their filter-bubble (and confirming research from previous decades). People become isolated in a filter-bubble of limited information sources, of which they are largely unaware.

    Diagram representing an online filter bubble allowing some types of information through, but not others.

    Figure 2. In an ideological filter bubble, indicated by the circle, exchange of information is closed, limited to a prescribed network of influences, and insulated from rebuttal (Wikipedia)

    Social media algorithms and network-association mechanisms (such as following people whose posts you prefer) can inadvertently isolate a user into an ideological filter bubble, by only serving them information that it thinks they want to see. It is important to actively seek out diverse sources of information and – when countering disinformation in a community – to introduce countervailing information (such as data on the efficacy of vaccination) via trusted community influencers, rather than presenting people with external, unvouched for scientific evidence.

    Notes

    [1] Kellyanne Conway, a public relations and media influencer working for Donald Trump, famously coined the phrase “alternative facts” to reflect ideological perspectives for which there was no objective evidence.
    https://en.wikipedia.org/wiki/Alternative_facts

    [2] An example of sentiment-analysis is associating a modifier such as “demented” with a keyword such as “president.” Posts containing both terms will be ranked as more attractive to the user than posts without them, if the user has “liked” posts with similar sentiment-terms previously.

    Reference

    Chatman, E. A. (1991). Life in a small world: Applicability of gratification theory to information-seeking behavior. Journal of the American Society for Information Science, 42, 438–449.
    https://doi.org/10.1002/(SICI)1097-4571(199107)42:6<438::AID-ASI6>3.0.CO;2-B

    Chowdhury, G. G., & Chowdhury, S. (2013). Human information behaviour studies and models. In Information Users and Usability in the Digital Age (pp. 55–84). Facet Publishing.
    https://doi.org/10.29085/9781856049757.004

    Gonza´lez-Teruel & Abad-Garcı´a (2018) The influence of Elfreda Chatman’s theories: a citation context analysisScientometrics (2018) 117:1793-1819
    https://doi.org/10.1007/s11192-018-2915-3

    Harmon-Jones, E., & Harmon-Jones, C. (2007). Cognitive dissonance theory after 50 years of development. Zeitschrift für Sozialpsychologie, 38(1), 7-16. Downloaded 5/12/2026 from
    https://www.researchgate.net/profile/Eddie-Harmon-Jones/publication/255581596_Cognitive_Dissonance_Theory_After_50_Years_of_Development/links/638e8e53484e65005be6c4a8/Cognitive-Dissonance-Theory-After-50-Years-of-Development.pdf

    Knobloch-Westerwick, S., Westerwick, A., & Sude, D. J. (2019). Media choice and selective exposure. In Media effects (pp. 146-162). Routledge. Downloaded 5/12/2026 from
    https://kimliaa.wordpress.com/wp-content/uploads/2021/10/routledge_communication_series_mary_beth_oliver_arthur_a._raney_jennings_bryant_-_media_effects__adv.pdf#page=157

    Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.

    Wikipedia (2015) Filter bubble. Accessed 5/12/2026 at https://en.wikipedia.org/wiki/Filter_bubble. Graphic source Original: Evbestie Vector: Dabmasterars, CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons

  • Actor-Network Theory

    Actor-Network Theory

    Sociomaterial Networks of Resources (Human and non-Human)

    A recent emphasis on sociomateriality appears to have entered the IS literature because of discussions by Orlikowski (2010) and the excellent empirical study of Volkoff et al. (2007). Now that people have been sensitized to the literature on material practice, actor-network theory is classified as “tired and uninformative” [1]. Which leads me to wonder just how many IS academics have actually read the actor-network theorists? Or pondered how this applies to technology design?

    Long before people started discussing socio-material “assemblages,” Bruno Latour (1987) and John Law (1987) were discussing how technology developed by means of “heterogeneous networks” of material and human actants, the combination of which directs the trajectory of technology design and form. Latour (1999) suggests that he should recall the term “actor-network,” as this is too easily confused with the world-wide web. Yet actor-networking – in the sense of a web of connectivity, where heterogeneous interactions between diverse individuals, between virtually-mediated groups, and between individuals and material forms of embedded intentionality – is exactly what is going on in today’s organizations.

    In addition, Michel Callon’s (1986) work on how the “problematization” of a situation in ways that aligns the interests of others leads to their enrolment in a network of support for a specific technological frame. Once support has been enrolled, such networks endow irreversibility, which makes changes to the accepted form of a technology solution incredibly difficult. So we have paradigms that are embedded in a specific design. Akrich coined the term “script” to define the performativity of technology and the term was adopted by the other leading actor-network theorists [2]. This thread of work articulates incredibly deeply the ways in which technology design directs its users (and maintainers) into a set of roles and worldviews that are difficult to escape. We must “de-script” technology to repurpose it to other networks and other applications – which is much more difficult than one would suppose, given the embedded social worlds that are carried across networks of practice with the use of common technologies (Akrich 1992).

    So what does actor-network theory give us?

    Actor-network theory provides a conceptual and practical approach to understanding and modeling why design takes specific forms – and what needs to be “undone” for a design to be conceived differently than in the past [3]. It provides a rationale for understanding technology as a network actor in its own right, influencing behavior and constraining discovery. The assumptional frameworks embedded in – for example – a software book-pricing application will direct the evaluation of price alternatives in ways that reflects the model of decision-making adopted by the software’s author. This results in the type of stupid automaticity that recently saw an Amazon book priced at $23,698,655.93 (plus $3.99 shipping). The cause of this pricing glitch was traced back to an actor-network of two competing sellers, unknowingly connected via their use of the same automated pricing software [4].

    A lot of the “materiality of practice” literature has identified new phenomena and new mechanisms of actor-networks, by exploring material (non-human) resources. For example Knorr Cetina (1999) has sensitized us to how epistemology is embedded in socio-technical networks. Rheinberger (1997) has demonstrated how some technical objects are associated with emergence while others enforce standardization around a prescribed framework or process. Henderson (1999) demonstrates how the use of specific representations can conscript others around the norms of an organizational power-group (e.g. IT developers who priorititize technical requirements over usability).

    These effects can be understood by using Actor-Network Theory as the epistemology underlying analysis of a design’s evolution. Exploring actor-network interactions reveals mechanisms that are relevant to how we work today.

    For further reading, I would strongly recommend Bruno Latour’s (2005) book, Reassembling The Social.

    Notes:

    [1] I have to declare an interest here – this comment was contained in a review of one of my papers … 🙂

    [2] As Latour (1992) argues: “Following Madeleine Akrich’s lead (Akrich 1992), we will speak only in terms of scripts or scenes or scenarios … played by human or nonhuman actants, which may be either figurative or nonfigurative.”

    [3] One of my favorite papers on the topic of irreversibility in design is ‘How The Refrigerator Got Its Hum,’ by Ruth Cowan (1995). Another good read is the introduction to the same book by MacKenzie and Wajcman (1999).

    [4] The amusing outcome is recounted by Michael Eisen, at http://www.michaeleisen.org/blog/?p=358

    References:

    Akrich, M. 1992. The De-Scription Of Technical Objects. W.E. Bijker, J. Law, eds. Shaping Technology/Building Society: Studies In Sociotechnical Change. MIT Press, Cambridge, MA, 205-224.

    Callon, M. 1986. “Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay.” J. Law, ed. Power, Action, and Belief: a New Sociology of Knowledge? Socioogical Review Monograph 32. Routledge and Kegan Paul, London, 196-233.

    Cowan, R.S. 1995. “How the Refrigerator Got its Hum.” D. Mackenzie, J. Wajcman, eds. The Social Shaping of Technology. Open University Press, Buckingham UK, 281-300.

    Henderson, K. 1999. On Line and on Paper: Visual Representations, Visual Culture,and Computer Graphics in Design Engineering. MIT Press, Harvard MA.

    Knorr Cetina, K.D. 1999. Epistemic Cultures: How the Sciences Make Knowledge. Harvard Univ. Press, Cambridge, MA.

    Latour, B. 1987. Science in Action. Harvard University Press, Cambridge MA.

    Latour, B. 1992. “Where Are the Missing Masses? The Sociology of a Few Mundane Artifacts.” W.E. Bijker, J. Law, eds. Shaping Technology/Building Society: Studies In Sociotechnical Change. MIT Press, Cambridge MA.

    Latour, B. 1999. “On Recalling ANT.” J. Law, J. Hassard, eds. Actor Network and After. Blackwell, Oxford, UK 15-25.

    Law, J. 1987. “Technology and Heterogeneous Engineering – The Case Of Portugese Expansion.” W.E. Bijker, T.P. Hughes, T.J. Pinch, eds. The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology. MIT Press, Cambridge MA.

    MacKenzie, D.A., J. Wajcman. 1999. Introductory Essay. D.A. Mackenzie, J. Wajcman, eds. The Social Shaping Of Technology, 2nd. ed. Open University Press, Milton Keynes UK, 3-27.

    Orlikowski, W. 2010. “The sociomateriality of organisational life: considering technology in management research.” Cambridge Journal of Economics 34(1) 125-141.
    Rheinberger, H.-J. 1997. Experimental Systems and Epistemic Things Toward a History of Epistemic Things: Synthesizing Proteins in the Test Tube. Stanford University Press, Stanford CA, 24-37.

    Volkoff, O., D.M. Strong, M.B. Elmes. 2007. “Technological Embeddedness and Organizational Change.” Organization Science 18(5) 832-848.