focalinterest.net

Tag: Communication & Public Relations

  • VGenAI favored minor-party adoption but not engagement gains

    What the study found

    The study found evidence for both equalization and normalization in the use of visual generative AI (AI that creates images and videos) during the 2025 German federal election. Minor parties used it more often than major parties, but the higher engagement linked to AI posts did not give minor parties a larger advantage.

    Why the authors say this matters

    The authors conclude that the findings help explain how emerging communication technologies interact with party system structures. They also say the results have implications for regulatory approaches to synthetic political content.

    What the researchers tested

    The researchers examined Facebook and Instagram posts from 37 German parties in the four weeks before election day. They used a semi-automated AI detection method that combined automated classification with manual validation, and analyzed nearly 1,000 visual generative AI images and videos from about 400 party accounts.

    What worked and what didn't

    On adoption, minor parties used visual generative AI at higher rates than major parties, which supports the equalization hypothesis that low-cost technologies can help resource-constrained actors produce professional campaign visuals. On content strategy, mainstream major parties disclosed AI origins more often than minor parties or the Alternative for Germany, and the Alternative for Germany stood out for extensive use of photorealistic imagery, citizen depictions, criminal portrayals, and negative tone. On engagement, visual generative AI posts were associated with higher user engagement than non-AI posts, but this advantage appeared to accrue equally to major and minor parties rather than reducing differences between them.

    What to keep in mind

    The abstract does not describe limitations in detail. The findings are based on one election, two platforms, and a four-week period before election day.

    • Minor parties used visual generative AI more often than major parties.
    • AI posts were associated with higher user engagement than non-AI posts.
    • That engagement advantage was similar for major and minor parties.
    • Mainstream major parties disclosed AI use more often than minor parties or the Alternative for Germany.
    • The Alternative for Germany used more photorealistic, citizen-focused, criminal, and negative imagery.

  • Weibo sentiment tracked Typhoon Muifa’s precipitation and impacts

    What the study found

    The study found that public opinion on Sina Weibo during Typhoon Muifa was closely linked to the typhoon’s activity. Four main discussion themes appeared: typhoon impact, weather conditions, meteorological information, and disaster response.

    Why the authors say this matters

    The authors conclude that social media can serve as a real-time indicator of localized public sentiment during disasters. They also suggest that official risk narratives play a key role in shaping public attention and that the findings may inform targeted risk communication and emergency management strategies.

    What the researchers tested

    The researchers analyzed 19,417 microblog posts from Sina Weibo about Typhoon Muifa, which made four landfalls in China. They used Latent Dirichlet Allocation (a topic-modeling method for finding themes in text), sentiment analysis, and correlation statistics to examine how public attention and discourse changed with the typhoon.

    What worked and what didn't

    Four dominant topic categories were identified, and personal accounts mainly discussed typhoon impact and weather conditions, while official accounts mainly covered meteorological information and disaster response. Daily total precipitation was strongly positively correlated with the number of microblog posts overall (R 2 = 0.84, q < 0.001), especially in Zhejiang, Shanghai, Shandong, and Liaoning (q < 0.05), and negative sentiment was also highly correlated with rising precipitation, largely through the typhoon impact topic.

    What to keep in mind

    The abstract does not describe limits beyond the study’s focus on Sina Weibo posts during one typhoon event. The findings therefore apply to this specific case study and the available summary does not report additional caveats.

    • The study examined 19,417 Sina Weibo posts about Typhoon Muifa.
    • Four main discussion themes were identified: typhoon impact, weather conditions, meteorological information, and disaster response.
    • Personal accounts were more common in impact and weather discussions, while official accounts dominated meteorological information and disaster response.
    • Daily precipitation was strongly positively correlated with the number of posts overall.
    • Negative sentiment also increased with rising precipitation, mainly in the typhoon impact topic.
  • Social norms and shared meaning support collective action in crises

    Social norms and shared meaning support collective action in crises

    What the study found

    The study argues that collective action is important during crises because people face shortages of time, resources, and choices. It also says that social conventions, norms, group cohesiveness, and shared meanings can help people address collective action problems.

    Why the authors say this matters

    The authors suggest that, in difficult situations, collaborative stakeholders need effective communication to make informed decisions and to engage with, manage, and recover from a crisis. They also state that technological advances and social media are changing how people respond to crises.

    What the researchers tested

    This is a perspective study. The researcher considers what is learned about crisis, decision-making, and sense-making in tumultuous situations, and reviews principles that underlie these ideas. The study also examines when and how shared meanings lead to more useful or adaptive behavior, and reviews enabling technologies and their potential in collective action.

    What worked and what didn't

    The paper says collective action becomes a priority when crises reduce time, resources, and options. It also indicates that group cohesiveness and shared meanings can make collective action problems easier to address, but it does not provide experimental comparisons or numerical results.

    What to keep in mind

    The available abstract presents a perspective and review, not a test of a specific intervention. It does not describe detailed methods, sample size, or measured outcomes.

    • Crises create major challenges for population-level decision-making.
    • The study says social norms and group cohesiveness can help resolve collective action problems.
    • Shared meanings are described as important for more adaptive behavior in crisis situations.
    • The authors suggest communication among stakeholders is needed for informed decisions and crisis recovery.
    • The paper also reviews enabling technologies and their potential role in collective action.

  • Social media text can support disaster response tracking

    What the study found

    The article says that mining social media text can be a valuable resource for disaster response. It also states that advanced natural language processing and machine learning can help extract relevant information while filtering noise and misinformation.

    Why the authors say this matters

    The authors suggest that social media can support disaster relief coordination and improve situational awareness during emergencies. They cite real-world cases, including Hurricanes Harvey, Ida, Milton, and Melissa, as examples of this role.

    What the researchers tested

    The research aims to develop a methodology that combines textual classification of social media data, spatial analysis, temporal analysis, and visual analytics. The abstract presents this as a way to provide rapid responses during natural disasters.

    What worked and what didn't

    The abstract reports that textual data from social media offers opportunities for disaster response when processed with NLP and machine learning. It also notes challenges: the data are unstructured and ambiguous, user credibility varies, and the volume of information can be overwhelming.

    What to keep in mind

    The available summary does not describe specific experiments, evaluation results, or performance measures. It also does not provide details on limitations beyond the general challenges of unstructured data, credibility differences, and high data volume.

    • Social media text is described as a valuable resource for disaster response.
    • Natural language processing and machine learning are said to help filter noise and misinformation.
    • The authors point to Hurricanes Harvey, Ida, Milton, and Melissa as real-world examples.
    • The proposed approach combines textual classification, spatial analysis, temporal analysis, and visual analytics.
    • The abstract notes challenges from ambiguous data, varying credibility, and large data volume.

  • Misleading crisis communication increases brand avoidance in hospitality

    What the study found

    The study found that misleading greenwashing crisis communication is linked to higher consumer brand avoidance in hospitality. It also found that omission is more damaging than paltering, which means giving information that is technically true but misleading.

    Why the authors say this matters

    The authors conclude that their findings support adding misleading tactics and sincerity-based pathways to Situational Crisis Communication Theory, which is a framework for understanding how organizations should respond to crises. They also say hospitality firms should avoid omission, communicate transparently, and assess perceived severity early because sincerity and trust restoration are crucial for reducing brand avoidance.

    What the researchers tested

    The researchers conducted two scenario-based online experimental studies. They used analysis of variance and the PROCESS macro to test direct, indirect, and moderated mediation effects, examining perceived response sincerity and brand trust as sequential mediators and perceived crisis severity as a moderator.

    What worked and what didn't

    Misleading crisis communication significantly increased consumer brand avoidance. The difference between omission and paltering appeared in the negative consumer responses, with omission producing stronger harm; this difference emerged only when perceived crisis severity was lower.

    What to keep in mind

    The abstract does not provide detailed limitations beyond noting the study's focus on greenwashing crises in hospitality and on crisis response strategies rather than prevention. The findings are based on scenario-based online experiments, so the summary does not describe how they would apply in other settings.

    • Misleading greenwashing crisis communication was associated with greater brand avoidance.
    • Omission was more damaging than paltering in shaping negative consumer responses.
    • Perceived response sincerity and brand trust were tested as sequential mediators.
    • Perceived crisis severity moderated the indirect pathway to brand avoidance.
    • The difference between omission and paltering appeared only at lower perceived severity.

  • Chinese and U.S. media framed the earthquake differently

    What the study found

    The study found that Chinese Central Television (CCTV) and Cable News Network (CNN) framed the 2023 Turkey-Syria earthquake in different ways shaped by their journalistic cultures. CCTV emphasized Chinese rescue efforts and Chinese benefactors, while CNN emphasized the suffering of distant victims and presented studio anchors and experts as detached observers.

    Why the authors say this matters

    The authors conclude that these differences reveal contrasting national approaches to international humanitarianism. They suggest the U.S. frames distant suffering within a liberal humanitarian imaginary that emphasizes moral obligation, while China constructs a narrative of benevolent power to legitimize its emerging role in global humanitarianism.

    What the researchers tested

    The article examined how journalistic cultures in China and the U.S. shaped media discourses on the Turkey-Syria earthquake. It used multimodal analysis and critical discourse analysis to compare CCTV and CNN coverage.

    What worked and what didn't

    CCTV coverage focused on scenes of suffering while centering the actions of Chinese rescue teams and other Chinese benefactors. CNN coverage directly portrayed victims' pain and included studio anchors and experts analyzing the disaster's broader impact from a more detached position.

    What to keep in mind

    The abstract does not provide sample size, time period, or detailed coding procedures. It also does not describe any limitations beyond the scope of comparing CCTV and CNN coverage of this single disaster.

    • The study compared CCTV and CNN coverage of the 2023 Turkey-Syria earthquake.
    • CCTV emphasized Chinese rescue efforts and Chinese benefactors amid suffering.
    • CNN emphasized the suffering of victims and used detached studio commentary.
    • The authors link the differences to contrasting journalistic cultures.
    • The authors say the findings point to different national approaches to international humanitarianism.
  • Contextual heuristics shape cognitive bias in online emergencies

    Contextual heuristics shape cognitive bias in online emergencies

    What the study found

    The study found that field situational heuristics, meaning context-based mental shortcuts, are positively associated with cognitive bias in online emergencies. It also found that adaptive expectations and implicit attributions act as mediating pathways in this relationship.

    Why the authors say this matters

    The authors say the findings provide theoretical insights for improving online public opinion governance and enhancing audience media literacy. The study suggests that understanding how situational heuristics shape cognitive outcomes in digital communication environments may help manage information dissemination during online emergencies.

    What the researchers tested

    The researchers built a framework from field theory and heuristic information processing theory. They used anchoring heuristics, representativeness heuristics, and availability heuristics as independent variables; cognitive bias as the dependent variable; and adaptive expectations and implicit attributions as mediators. Data came from questionnaires and were analyzed with structural equation modeling using AMOS 22.0.

    What worked and what didn't

    Anchoring heuristics, representativeness heuristics, and availability heuristics all showed significantly positive effects on cognitive bias, with mediation through adaptive expectations and implicit attributions. Representativeness heuristics had the largest effect, followed by availability heuristics and then anchoring heuristics. The effect of contextual heuristics on cognitive bias also showed significant demographic differences between and within groups.

    What to keep in mind

    The abstract does not describe sample size, questionnaire details, or other study limitations. The summary also does not provide enough information to judge how broadly the findings apply beyond the online emergency context studied.

    • Field situational heuristics were positively related to cognitive bias in online emergencies.
    • Adaptive expectations and implicit attributions mediated the relationship between heuristics and cognitive bias.
    • Representativeness heuristics had the strongest effect among the three heuristics studied.
    • Availability heuristics had the second-strongest effect, and anchoring heuristics the weakest.
    • The effects differed significantly across demographic groups.