Interactive Nature of Social Media’s Comment Feature
Bibliographic record
Abstract
Discussions through interactions between contending parties have been known to have minimised, if not completely resolved, many conflicts, and have nipped numerous others in the bud because people were able to express themselves for others to know their stands on issues. Likewise, new media technologies, ably hinged on the Internet, have further created avenues for more interactions among people in different media ecosystems. Given the variegated attributes of the Internet, most newspapers now have online versions which have provisions for readers to make comments at the end of each story or report. The comment feature of online newspapers and social media gives room for interaction among readers and users, hence, commenters are not only using it to comment on what they consume from the media, but they also use it to react and comment on the comments made by other commenters. This brings about a robust social interaction among the commenters, outside the medium that serves as the source of news or topic of discussion. In October 2020, youth in Nigeria embarked on a protest against police brutality tagged #EndSARS, SARS being the acronym for Special Anti-Robbery Squad of the Nigerian police. The youth mobilised themselves nationwide through social media and other Internet platforms to hold rallies and protests, with the major protest taking place at the Lekki Tollgate in Lagos. It is within this context that this paper looked at the social interaction that took place among commenters who commented in Sahara Reporters, Premium Times, and the online version of The Punch newspaper on the #EndSARS issue. The objectives were to find out how many comments were made in the comment sections of these selected online newspapers as they relate to their reports on #EndSARS; to ascertain how many of the comments were socially interactive, and to determine the extent the comments proffered solutions to police brutality in Nigeria. Grounded in the Social Network Theory, the study utilised content analysis and direct observation methods to gather data for evaluation while coding sheets and coding guide were used as data collection instruments. Findings revealed that commenters were engaged in interactive discussions among themselves when expressing their opinions about the #EndSARS protests. It was also discovered that some of the comments proffered solutions to the issue of police brutality, and how it can be addressed. The paper concluded that the comment feature of social media is another unique avenue for citizens to voice out their opinions, and to reach out to, and engage the “high and mighty” in the society, either within or outside government, they might not be privileged to reach through other means. Based on the findings, it was recommended, among others, that those in government, particularly in developing countries such as Nigeria should pay critical attention to the comment sections of various social media to have an idea of what the populace feel about their polices based on the report about them that citizens read in the media.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.021 |
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".