1. SciScore for 10.1101/2021.07.15.21260543: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    For calculation of the index, we included the racial and ethnic categories: Latino/Hispanic, American Indian and Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, and Non-Hispanic White [49].
    Non-Hispanic White
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite the merits of our study, we want to point out the following weaknesses and future research directions: 1) While a major strength of the lexicon-based sentiment classification approach lies in its simplicity, this method does not allow us to identify more complicated language features, such as sentiment shifters (e.g., “I don’t like this car”, is negative, even though the word “like” is not; [15]); 2) Twitter users represent a younger demographic group, whose sentiments and opinions may not reflect those of the entire population. In addition, urban areas tend to be overrepresented in tweet samples [63]. We tried to partially address this issue by including the proportion of the population between 18 and 34 (the main demographic who uses Twitter) but discarded the variable during our modelling process due to unacceptably high correlations with other variables on our model. 3) Our regression modelling approach does not consider the temporal dimension (except for the 1st death variable), despite having a spatiotemporally complete dataset. Therefore, our current and future research efforts focus on the application of spatiotemporally explicit modelling using Bayesian statistics to address the spatial and temporal nature of our dataset [64]. Lastly, due to the real-time availability of data, such as tweets and various metrics on COVID-19, it is feasible to apply our methods and update the results of this study daily. For instance, the space-time scan statistic can be employ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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