Adversaries have several ways to leverage and expose the data disclosed in social media network engagements for different motives including but not limited to fraud, discreditation, or social engineering. Previous research on social media interactions discussed increased engagements where influencers and viral trends were involved. Studies also discussed engagements declining over time. Within posts or engagements, personal identifiable information (PII) can be shared with varying rate of risk severity. Publicly available data such as this can be leveraged by adversaries. With the absence of insights of whether influencers impact engagements and disclosures, the goal of this study was to obtain these insights, diving into whether influencer power (reach) and influencer tier (size based on followers) positively correlated with engagements and disclosures, and whether engagements and disclosures declined over time. A research instrument, SoCap, was developed to collect and analyze social posts using a Design Science Research approach. Twitter (X) was used as the data source in the pilot analysis, but due to unprecedented platform changes, Reddit was used in the main study. The instrument along with the insights are the primary contributions to the field. Data analysis showed significant positive correlations between influence power (eigenvector centrality) and both engagements and disclosures. Engagements and disclosures were found to decline over time with 76.50% and 79.23% of conversations respectively (N=183) presenting statistically significant declines in the main study. All six hypotheses were supported with statistically significant results. Due to platform differences and reliability concerns, caution is advised with influencer tier insights where Reddit is concerned. Furthermore, a small set of conversations were initiated by non- influencers, an implication that non-influencers can produce viral conversations, but most conversations were initiated by influencers and an average of 29.41% of the 122,904 posts within those conversations contained PII disclosures. Future study recommendations included alternative influencer identification methods and PII detection methods, a call for a more unified approach towards leveraging methods across platforms to improve reliability, and an approach for a subjective study comparing idealized behavior with actual behavior.