Networked Affect
Abstract
Investigations of affective experiences that emerge in online settings that range from Facebook discussion forums to “smart” classrooms. Our encounters with websites, avatars, videos, mobile apps, discussion forums, GIFs, and nonhuman intelligent agents allow us to experience sensations of connectivity, interest, desire, and attachment—as well as detachment, boredom, fear, and shame. Some affective online encounters may arouse complex, contradictory feelings that resist dualistic distinctions. In this book, leading scholars examine the fluctuating and altering dynamics of affect that give shape to online connections and disconnections. Doing so, they tie issues of circulation and connectivity to theorizations of networked affect. Their diverse investigations—considering subjects that range from online sexual dynamics to the liveliness of computer code—demonstrate the value of affect theories for Internet studies. The contributors investigate networked affect in terms of intensity, sensation, and value. They explore online intensities that range from Tumblr practices in LGBTQ communities to visceral reactions to animated avatars; examine the affective materiality of software in such platforms as steampunk culture and nonprofit altporn; and analyze the ascription of value to online activities including the GTD (“getting things done”) movement and the accumulation of personal digital materials. Contributors James Ash, Alex Cho, Jodi Dean, Melissa Gregg, Ken Hillis, Kylie Jarrett, Tero Karppi, Stephen Maddison, Susanna Paasonen, Jussi Parikka, Michael Petit, Jennifer Pybus, Jenny Sundén, Veronika Tzankova
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Online, affections are embodied in a set of signs that contribute to the constitution of an affecting public and affected networks.
“Online, these affections are embodied in a set of signs that contribute to the constitution of an affecting public and affected networks (Hillis, Paasonen, & Petit, 2015).”
From Beyond Verification| Inauthentic Authenticity: Astroturfing to Generate True Affect Using Fakery (2026) · cites this paper as Hillis, Paasonen, & Petit, 2015
Cited by
Sorted by citations
Remixing misandry, manspreading, and dick pics: networked feminist humour on Tumblr
Ringrose, J., & Lawrence, E. (2018) · Feminist Media Studies · doi:10.1080/14680777.2018.1450351 · Cited by 88 · see citing works
Beyond Verification| Inauthentic Authenticity: Astroturfing to Generate True Affect Using Fakery
Alloing, C., Fournier, V., Reid, V., Germain, S., Turbide, O., & Yates, S. (2026) · International Journal of Communication · doi:10.65476/qd17zh66
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2015-02-27
- OpenAlex
- W4251905478
- Cited by
- 138
- References
- 0
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 12 · 2023: 4 · 2022: 9 · 2021: 15 · 2020: 22 · 2019: 12 · 2018: 15 · 2017: 12 · 2016: 11 · 2015: 1
- FWCI
- 17.32
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- The MIT Press eBooks (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Social Sciences › Communication › Cultural Studies and Postmodernism
- Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
- Keywords
- affect theory, internet access, avatars, animated avatars, Tumblr, LGBTQ+ people
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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