Politicians & the public: The analysis of political communication in social media
Bibliographic record
Abstract
ABSTRACT Nowadays, social media has a pivotal role in political communication. Politicians, parties, and the public engage in social networks like Twitter or Facebook. This panel focuses on election campaigns and policy‐making process in social media. How do politicians use social media during elections? How can we identify the public opinion of voters through the application of text‐mining in social media? The US presidential election in 2016, possess big discussions and critics about the general social media usage in the context of election campaigns. Considering the case of Cambridge Analytica, information leakage, privacy issues, and trust also play an essential role as well. In respect of truthfulness, how can we encourage more robust and wide‐reaching sharing of trustworthy material, such as scholarly research? Besides politicians, the public comes more and more into the focus as a political stakeholder in social media. How does the public engage in policy‐making? Electronic‐petitioning serves as a medium to mobilize support and interest of the public. The panel provides the possibility for speaker and participants to exchange both, information as well as methodological approaches of political communication in social media.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".