The phenomenology of sharing: social media networking, asserting, and telling

• Author: Nicholas C. Zingale
• Published date: 01 August 2013
• DOI: http://doi.org/10.1002/pa.1468
• Date: 01 August 2013

■Academic Paper

The phenomenology of sharing: social

media networking, asserting, and telling

Nicholas C. Zingale*

Maxine Goodman Levin College of Urban Affairs, Cleveland State University, Cleveland, Ohio, USA

This article takes up the current promise of computer-aided social networks as mechanisms for sharing in experiences.

The author examines social networks phenomenologically, not merely as a tool for providing information and shaping

what we think but as a social construct for what can be shared, how we think, and what can be known. The analysis

identifies a connection between social networks and artificial intelligence systems, while also suggesting that signifi-

cant experiential gaps built into the systems can lead to distortions in the ontology of shared experiences. The author

argues, by applying concepts from Kant, Arendt, Schutz, and Heidegger, that computerized social networks offer an

unparalleled opportunity for public administrators to discover and learn about social conditions, but these networks

are not without significant limitations. An appreciation for the limits to sharing implicit in computerized social

networks and artificial intelligence systems can be made explicit by applying concepts from phenomenology.

Copyright © 2013 John Wiley & Sons, Ltd.

INTRODUCTION

Social media is here to stay. There is no question

about that, especially after Facebook reached

1 billion users and Twitter surpassed the

500 million-account mark (Facebook, 2013; Twitter,

2013). What is less clear, however, is how govern-

ment organizations can respond to the changing

communication demands of citizens who want

government to use social media in a meaningful,

interactive and engaging fashion (Mergel, 2012).

Built on the platform of neural networking

systems, social networking is the latest rendering

of artificial intelligence (AI) designed to mimic the

inter-workings of the human brain by acquiring

knowledge through a networked learning process

and using interneuron connection strengths to store

the acquired knowledge and identify patterns

(Ramlall, 2010). So seductive are social neural

networking systems, they can be found impinging

on nearly every aspect of humanity, similar to

what Albert Borgmann (1984) referred to as a tech-

deterministic device paradigm, ranging from individ-

ual psyche and pleasure to societal democracy,

freedom, and public participation (Surowiecki,

2004; Shirky, 2008; Howe, 2009; Mergel et al., 2009).

This project considers the limits of social neural

networking systems when sharing and experienc-

ing situations. It begins with a reminder of the

failed assumptions that brought down the 1970s

Massachusetts Institute of Technology Good Old

Fashion Artificial Intelligence program and argues

that, although social neural networking systems

offer an improved technological approach for

public engagement and participation, they are not

capable of sufficiently addressing the ontological

gaps associated withthe wholeness of an experience.

This is mainly because social neural networking

systems, such as AI, pervert what it means to share

in experiences in what Alfred Schutz called the

‘concrete we’relationship and Martin Heidegger

referred to as ‘absorbed coping’.

In short, as much as social neural networking is a

powerful tool, to use a technology term, it is not with-

out significant limitations. This argument rests on

two phenomenological fronts: (i) when separated

*Correspondence to: Nicholas C. Zingale, Maxine Goodman

Levin College of Urban Affairs, Cleveland State University, 2121

Euclid Avenue, UR 320 Cleveland, Ohio 44115, USA.

E-mail: n.zingale@csuohio.edu

Journal of Public Affairs

Volume 13 Number 3 pp 288–297 (2013)

Published online 30 May 2013 in Wiley Online Library

(www.wileyonlinelibrary.com) DOI: 10.1002/pa.1468

Copyright © 2013 John Wiley & Sons, Ltd.