In a recent SIGMOD paper titled "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study", Arora et al. [1] undertake a performance benchmarking study of several well-known algorithms for influence maximization. In the process, they contradict several published results, and claim to have unearthed and debunked several "myths" that existed around the research of influence maximization. It is the goal of this article to examine their claims objectively and critically, and refute the erroneous ones. Our investigation discovers that first, the overall experimental methodology in Arora et al. [1] is flawed and leads to scientifically incorrect conclusions. Second, the paper [1] is riddled with issues specific to a variety of influence maximization algorithms, including buggy experiments, and draws many misleading conclusions regarding those algorithms. Importantly, they fail to appreciate the trade-off between running time and solution quality, and did not incorporate it correctly in their experimental methodology. In this article, we systematically point out the issues present in [1] and refute 11 of their misclaims.