Framing crisis: news media narratives of the “Opi-void” (Opioid) epidemic on Twitter
Bibliographic record
Abstract
The following Major Research Paper (MRP) focuses on the discussion of opioids in Canada, online news outlets, and social media. More specifically, this research focuses on the Canadian Broadcasting Corporation (CBC), and how the organization frames the opioid crisis on Twitter through @CBCNews. This research excludes other CBC Twitter accounts (i.e., @CBC, @CBCAlerts, @CBCOttawa, @CBCToronto, @CBCManitoba, @CBCPolitics, @CBCCanada), as @CBCNews is the most active with 2.62 million followers. The following discussion considers the opioid discussion from a crisis communication lens. This research asserts that there is an apparent opioid crisis, given the situation’s complexity, and the number of opioid-related deaths. This research questions how social media (specifically Twitter) act as a tool for information dissemination during a health-related crisis, and how external factors (i.e., public opinion, bias, and current affairs) shape news content online. Without understanding the narrative (i.e., how a story is intentionally told) and strategies behind social media posts, news outlets like the CBC can promote hidden agendas and ideals (without a large amount of public knowledge or opposition). The CBC has goals, commitments, and preconceived notions like any other private organization. This reality is incredibly problematic during a public health crisis, as human lives depend on appropriate and trustworthy information. Instead of discussing an issue without bias or pre-conception, news outlets may provide subjective, false or vague information, which could lead to negative repercussions (Kim & Hyojung, 2017). Due to private motivations, the intent to control the opioid conversation (through politically-driven content, stigma-driven content or to even place blame, for example) or the promotion of ideas beneficial to pharmaceutical companies, for example, do news sources frame Twitter posts with a specific narrative in mind? Instead of analyzing social media as an enabler or an initiator of framing crisis online, this research focuses on how news outlets frame crises through social media as one of many possible media channels. Examining how social media as a platform acts as an echo chamber (therefore enabling an intended narrative) is an interesting concept. However, this type of analysis is beyond the scope of this research. As a result, the following Major Research Paper explores the following primary research questions: 1. How does the CBC frame the issue of opioid usage in Canada? In addition, why are their Twitter posts framed a certain way? 2. What is the nature of the dialogue occurring in response to the CBC’s Twitter coverage? What strategies are most conducive to audience response? 3. How are fluctuations and outliers in news coverage accounted for by the CBC? Do socially and/or politically driven events impact the timing of posts? Keywords: social media, crisis, communication, opioid crisis, strategy, political, narrative, framing, Canada, fentanyl, naloxone.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".