The authors test five theoretically derived hypotheses about what drives video ad sharing across multiple social media platforms. Two independent field studies test these hypotheses using 11 emotions and over 60 ad characteristics. The results are consistent with theory and robust across studies. Information-focused content has a significantly negative effect on sharing, except in risky contexts. Positive emotions of amusement, excitement, inspiration, and warmth positively affect sharing. Various drama elements such as surprise, plot, and characters, including babies, animals, and celebrities arouse emotions. Prominent (early vs. late, long vs. short duration, persistent vs. pulsing) placement of brand names hurts sharing. Emotional ads are shared more on general platforms (Facebook, Google+, Twitter) than on LinkedIn, and the reverse holds for informational ads. Sharing is also greatest when ad length is moderate (1.2 to 1.7 minutes). Contrary to these findings, ads use information more than emotions, celebrities more than babies or animals, prominent brand placement, little surprise, and very short or very long ads. A third study shows that the identified drivers predict sharing accurately in an entirely independent sample.

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While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer’s disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer’s DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.

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