Research Topic

Improving Human-Machine Feedback Loops in Social Networks

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About this Research Topic

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Online social networks are platforms that people use to build and maintain social relationships and/or share updates, news, opinions, works of creativity, and other objects of interest with Facebook, Twitter, and Instagram being the most notable. Online platforms where items such as videos, music, and hotels to name a few are shared, rated, and/or commented upon can also be considered as social networks. A common criticism is that 'the algorithm' of a given social network determines which items users will see and react upon. However, can it be true that, for example, Facebook completely determines with whom users interact? Is YouTube solely responsible if users are lured into pools of misinformation and falsehoods? Are filter bubbles purely algorithmic? A more human-centered approach to these issues acknowledges that the behavior of these algorithms is just as well shaped by the social (mediated) interactions between users and user-provided content and the way users react to them.

 

In any system where algorithms employ previous user actions for predicting, supporting, or recommending future actions of the same user or other users, feedback loops are created. The actions of these future users, influenced by the system, will influence system behavior for later generations of users. In terms of second-order cybernetics, both users and systems are responsible for control and communication within the process. Ultimately, it is the role of the designers to design, create, and adapt interfaces to enable and improve these processes. For so doing, designers need to better understand how human-system-human feedback loops work alongside the potential effects that various technological or social interventions may have.

In this Research Topic, we aim to investigate the mechanisms of social media responding to their users and vice versa. We invite original work that investigates the algorithmically moderated interaction between users. Examples of topics include but are not limited to the following:

●  What triggers cause users to spontaneously comment, rate, share, or otherwise react to posts, videos, or other (commercial, recommended) content?
●  How do users respond to online behavior that they consider unacceptable - and does this make a difference if the other users are strangers, friends, or even close relatives?
●  How do folk theories (possibly incorrect or incomplete mental models on how a system works) influence system usage and how can misconceptions be repaired?
●  What is the effect of the introduction of new interface elements, for example, the possibility to react with various emojis instead of a simple like or thumbs-up?
●  How can (personalized, intelligent) algorithms detect unexpected, undesirable responses (automatically or by means of user feedback or reports)?


Keywords: filter bubbles, echo chambers, second-order, cybernetics, social media, personalization, human-computer interaction


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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