Social Informatics

Abstract

Abstract. This paper suggests a method to map the evolution of technological trajectories by using unstructured text data. Combining techniques from the fields of natural language processing and network analysis, we are able to identify technological fields as overlapping communities of knowledge fragments. Over time persistence of these fragments allows to observe how these fields evolve into trajectories, which may change, split, merge and finally disappear. As empirical example we use the broad area of Technological Singularity, a umbrella term for different technologies ranging from neuroscience to machine learning and bioengineering which are seen as main contributors to the development of artificial intelligence and human enhancement technologies. Using a socially enhanced search routine, we extract 1,398 documents for the years 2011-2013. While we can identify consistent technology fields in static document collections, more advanced ontology reconciliation is needed to be able to track a larger number of communities over time.

Participation in the Sharing Economy

“…At the micro level, there is some (small) evidence of its production within homophilic networks (Schor et al, 2015). On a more macro scale, there is some evidence of its disruption as a consequence of the dis-intermediating effect of algorithms (Parigi & State, 2014). Still at the macro level, there is no relevant research on incremental (or detrimental) effects of sharing economy platforms on social capital in a given area.…”

“…In the case of larger scale initiatives, Miralles, Dentoni and Pascucci (2017), while surveying neo-rural food production networks in Spain, find that the connection between surveyed producers is able to produce interpersonal trust between different producers, thus allowing the whole network to function. Scaling up the size of the initiatives, Parigi and State (2014) find that, while this is not the case anymore today, originally, Couchsurfing usage was related to the creation of long-lasting ties between guests and hosts. At the same scale, there is some evidence by Airbnb (2015) that participation in the network has a positive outcome on interpersonal trust at both the individual and neighborhood level; however these claims could not be verified by independent researchers.…”

“…At the micro-level, some evidence exists on sharing economy experiments, whether they are organized as digital platforms or local non-digital initiatives (e.g., food and time banks), showing that they can enhance social connectivity. For example, Parigi and State (2014), studying Couchsurfing, found that sharing hospitality increases the likelihood of reciprocal ties between host and guest.…”

“…894) Dubois, Schor, and Carfagna (2014) find that, even in the case of small scale initiatives, people may be unwilling to use their most prized skills within a time bank as they feel they could fetch a bigger payoff on the market. As a matter of fact, the longitudinal analysis by Parigi and State (2014) find that, as a consequence of the diffusion of Couchsurfing as a platform and its increasing reliance upon digital intermediation, relations between users may have become progressively weaker.…”

“…Gandini, Pais and Beraldo (2016) find that, while algorithms are rather good at producing individual reputation, they perform poorly at building professional trust, as past interactions with a given customer are a worse predictor of economic success than overall algorithmically created reputation. Parigi and State (2014) have authored the only longitudinal study on trust in the sharing economy. Their conclusion is that the commercial twist of Couchsurfing, as well as its success, has significantly damaged the trust building potential of the platform at an interpersonal level.…”