Bibliographic record
Abstract
Social networking sites have had an indisputable impact on the realm of political communications, increasing political discourses all around the world. Social media platforms offer some affordances that can be employed by users to facilitate their political activism. However, online political activism can be influenced by software robots which can interact on social media to emulate human users’ behaviour and influence their political opinion. This dissertation employs the theory of affordances and explores how the affordances of Twitter, including shareability, direct communication, dynamic interaction, searchability, and identifiability function in practice and have been used to facilitate the political engagement of human and nonhuman users. To do so, this dissertation uses the case study of the 2017 Women’s March in the United States to articulate the affordances of Twitter and explores how human users employ the affordances of this platform in their political engagement. The result of this analysis indicates that the affordances of Twitter such as shareability (tweeting, retweeting, hashtags), and dynamic interaction (liking) were the most prevalent affordances of this platform during the Women’s March. In addition, the case study of “building the wall” is used to analyze social bots’ function, behaviour, and interaction on Twitter, to demonstrate to what extend bots are capable of using the affordances of Twitter. This study demonstrates that bots use the affordance of shareability (retweeting) and dynamic interaction (liking) more than other affordances. However, this study did not find convincing evidence that these bots are able to generate new content, cultivate meaningful discussions, or directly provide responses to other tweets. The theory of affordances accredits two main components, the human and the environment, and claims that affordances are properties that the environment offers to any human who perceives and uses them. However, focusing on two main elements creates substantial limitations for employing the theory to explore artificial intelligence and self-improving machines. This study demonstrates that bots are capable of using some of the affordances of Twitter and imitate human-like performance to some degree. Therefore, by including a third component — nonhuman smart actors— this study proposes a new definition for the theory of affordances, which is a core contribution of this dissertation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".