  It is a recurring problem of online communication that the properties of
unknown people are hard to assess. This may lead to various issues such as the
spread of `fake news' from untrustworthy sources. In sociology the sum of
(social) resources available to a person through their social network is often
described as social capital. In this article, we look at social capital from a
different angle. Instead of evaluating the advantage that people have because
of their membership in a certain group, we investigate various ways to infer
the social capital a person adds or may add to the network, their contributive
social capital (CSC). As there is no consensus in the literature on what the
social capital of a person exactly consists of, we look at various related
properties: expertise, reputation, trustworthiness, and influence. The analysis
of these features is investigated for five different sources of online data:
microblogging (e.g., Twitter), social networking platforms (e.g., Facebook),
direct communication (e.g., email), scientometrics, and threaded discussion
boards (e.g., Reddit). In each field we discuss recent publications and put a
focus on the data sources used, the algorithms implemented, and the performance
evaluation. The findings are compared and set in context to contributive social
capital extraction. The analysis algorithms are based on individual features
(e.g., followers on Twitter), ratios thereof, or a person's centrality measures
(e.g., PageRank). The machine learning approaches, such as straightforward
classifiers (e.g., support vector machines) use ground truths that are
connected to social capital. The discussion of these methods is intended to
facilitate research on the topic by identifying relevant data sources and the
best suited algorithms, and by providing tested methods for the evaluation of
findings.
