Bibliographic record
Abstract
Among the social media, Twitter is widely used by political leaders and the public to express opinions about various political issues. These tweets may influence the course of major political events like elections, Brexit and popularity of certain politicians. This common observation led to the research question of this paper: What are the political implications of Twitter postings? To answer this research question, an exploratory qualitative review of tweets was undertaken. Google Scholar was searched using the topic itself as the search term twice with two different time frames till 2019. The search yielded 41 papers. The papers were listed with brief description. A table categorising the methods used in the papers was useful to derive some conclusions. Generally, Twitter can be used for both positive and negative purposes and it can impact either or both the leader and the people who read them. Certain factors are involved in determining the nature of post and its outcomes. Many theories and methods had been used in the papers. Manual, machine learning and automatic analytical tools have been tested and used widely. None of the methods is perfectly suitable for all types of Twitter analysis. General content textual descriptions, characteristics of the texts, symbols, hidden meanings and presentation methods have been used in the tweets examined by the authors. The potential of negative tweets and hate speeches is quite clear. Absence of internal standard definition of these types of posts stands in the way of effective prevention. Some recommendations have been listed based on the findings of this review. A few limitations of this review have also been listed.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".