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A large variety of complex systems in ecology, climate science, biomedicine, and engineering have been observed to exhibit so-called tipping points, where the dynamical state of the system abruptly changes. Typical examples are the rapid transition in lakes from clear to turbid conditions or the sudden extinction of species after a slightly change of environmental conditions. Data and models suggest that detectable warning signs may precede some, though clearly not all, of these drastic events. This view is also corroborated by recently developed abstract mathematical theory for systems, where processes evolve at different rates and are subject to internal and/or external stochastic perturbations.
One main idea to derive warning signs is to monitor the fluctuations of the dynamical process by calculating the variance of a suitable monitoring variable. When the tipping point is approached via a slowly-drifting parameter, the stabilizing effects of the system slowly diminish and the noisy fluctuations increase via certain well-defined scaling laws.
Based upon these observations, it is natural to ask, whether these scaling laws are also present in human social networks and can allow us to make predictions about future events. This is an exciting open problem, to which at present only highly speculative answers can be given. It is indeed to predict a priori unknown events in a social system. Therefore, as an initial step, we try to reduce the problem to a much simpler problem to understand whether the same mechanisms, which have been observed in the context of natural sciences and engineering, could also be present in sociological domains.
Courtesy of Christian Kuehn.
In our work, we provide a very first step towards tackling a substantially simpler question by focusing on a priori known events. We analyse a social media data set with a focus on classical variance and autocorrelation scaling law warning signs. In particular, we consider a few events, which are known to occur on a specific time of the year, e.g., Christmas, Halloween, and Thanksgiving. Then we consider time series of the frequency of Twitter hashtags related to the considered events a few weeks before the actual event, but excluding the event date itself and some time period before it.
Now suppose we do not know that a dramatic spike in the number of Twitter hashtags, such as #xmas or #thanksgiving, will occur on the actual event date. Are there signs of the same stochastic scaling laws observed in other dynamical systems visible some time before the event? The more fundamental question is: Are there similarities to known warning signs from other areas also present in social media data?
We answer this question affirmatively as we find that the a priori known events mentioned above are preceded by variance and autocorrelation growth (see Figure). Nevertheless, we are still very far from actually using social networks to predict the occurrence of many other drastic events. For example, it can also be shown that many spikes in Twitter activity are not predictable through variance and autocorrelation growth. Hence, a lot more research is needed to distinguish different dynamical processes that lead to large outburst of activity on social media.
The findings suggest that further investigations of dynamical processes in social media would be worthwhile. Currently, a main focus in the research on social networks lies on structural questions, such as: Who connects to whom? How many connections do we have on average? Who are the hubs in social media? However, if one takes dynamical processes on the network, as well as the changing dynamics of the network topology, into account, one may obtain a much clearer picture, how social systems compare and relate to classical problems in physics, chemistry, biology and engineering.
In 2006, there appeared to be a remarkable consensus among Internet gurus, activists, bloggers, and academics about the promise of Web 2.0 that users would attain more power than they ever had in the era of mass media. Rapidly growing platforms like Facebook (2004), YouTube (2005), and Twitter (2006) facilitated users’ desire to make connections and exchange self-generated content. The belief in social media as technologies of a new “participatory” culture was echoed by habitual tools-turned-into-verbs: buttons for liking, trending, following, sharing, trending, et cetera. They articulated a feeling of connectedness and collectivity, strongly resonating the belief that social media enhanced the democratic input of individuals and communities. According to some, Web 2.0 and its ensuing range of platforms formed a unique chance to return the “public sphere” — a sphere that had come to be polluted by commercial media conglomerates — back in the hands of ordinary citizens.
Eight years after the apex of techno-utopian celebration, a number of large platforms have come to dominate a social media ecosystem vastly different from when the platforms just started to evolve. It’s time for a reality check. What did social media do for the public — users like you — and for the ideal of a more democratic public space? Do they indeed promote connectedness and participation in community-driven activities or are they rather engines of connectivity, driven by automated algorithms and invisible business models? Online socializing, as it now seems, is inimically mediated by a techno-economic logic anchored in the principles of popularity and winner-takes-all principles that enhance the pervasive logic of mass media instead of offering alternatives.
Most contemporary social media giants once started out as informal platforms for networking or “friending” (Facebook), for exchanging user-generated content (YouTube), or for participating in opinionated discussions (Twitter). It was generally assumed that in the new social media space, all users were equal. However, platforms’ algorithms measured relevance and importance in terms of popularity rankings, which subsequently formed the quantifiable basis of data-driven interactivity wrapped in “social” rhetoric such as following, trending, or sharing. In this platform-mediated ecosystem, sponsored and professionally generated content soon received a lot more attention than user-generated content. Platforms like YouTube and Facebook gradually changed their interfaces to yield business models that were staked in two basic variables: attention and user data. By 2012, once informal social traffic between users had become fully formalized, automated, and commoditized by platforms owned and exploited by fast growing corporate giants. Although each of these platforms nurses its own proprietary mechanisms, they are staked in the same values or principles: popularity, hierarchical ranking, quick growth, large traffic volumes, fast turnovers, and personalized recommendations. A like is not a retweet, but most algorithms are underpinned by the norms of popularity and fast-trending topics.
The cultivation of online sociality is increasingly dominated by four major chains of platforms: Google, Apple, Facebook, and Amazon. These chains share some operational principles even if they differ on some ideological premises (open versus closed systems). Some consider social media platforms as alternatives to the old mass media, praising their potential to empower individual users who can contribute their own opinions or content to a media universe that was before pretty much closed to amateurs. Although we should not underestimate this newly acquired power of the web as a publishing medium for all, it is hard to keep up the tenet that social media are alternatives to mass media. Over the past few years, it has become increasingly obvious that the logics of mass media and social media are intimately intertwined. Not just on the level of platforms mechanics and content (tweets have become the equivalent of soundbites) but also on the level of user dynamics and business models; YouTube-Google now collaborates with many former foes from Hollywood to turn their platform into the gateway to the entertainment universe. Newspapers and television stations are inevitably integrated in the ecosystem of connective media where the mechanisms of data-driven user traffic determines who and what gets most attention, hence drawing customers and eyeballs.
This new connective media system has reshaped the power relationships between platform owners and users, not only in terms of who may steer information but also who controls the vast amount of user data that rushes through the combined platforms every day. What are the larger political and social concerns behind deceptively simple interfaces and celebrated user-convenient tools? Where in 2006 the notion of user power still seemed unproblematic, the relationship between users and owners of social media platforms is now contentious and embattled. In the wake of the growing monopolization of niches (Facebook for social networking, Google for search, Twitter for microblogging) it is important to redefine and reappraise the meaning of “social,” “public,” “community,” and “nonprofit.” The ecosystem of connective media has no separate spaces for the “public”; it is a nirvana of interoperability which major players argue for deregulation and which imposes American neoliberal conditions on a global space where boundaries are considered disruptions of user convenience. Common public values, such as independence, trust, or equal opportunities, are ready for reassessment if they need to survive in an environment that is defined by social media logic.
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The other day, I glanced at my Pandora, and it hit me that the singer Jason Derulo was actually Jay Sean Derulo. (If you don’t know who I’m talking about, good for you.) I was amazed – how had I not realized this before?! Because it’s not true. They’re actually two different people, but at least in looking it up, I stumbled upon this gem of a mash-up. [YouTube]
Eight years after the apex of techno-utopian celebration, a number of large platforms have come to dominate a social media ecosystem vastly different from when the platforms just started to evolve. It’s time for a reality check. What did social media do for the public — users like you — and for the ideal of a more democratic public space? Do they indeed promote connectedness and participation in community-driven activities or are they rather engines of connectivity, driven by automated algorithms and invisible business models? Online socializing, as it now seems, is inimically mediated by a techno-economic logic anchored in the principles of popularity and winner-takes-all principles that enhance the pervasive logic of mass media instead of offering alternatives.