I'm fitting a linear regression model on a dataset about how many upvotes a certain post will get based on its views, its author's reputation ecc. To satisfy the normality assumptions I performed a boxCox transformation and subsequently log-transformed the response: I obtained a p-value of $0.3$ in a Shapiro test and a fairly good looking qq-plot.
However when checking for residuals homoscedasticity I get the following plots:
On the left I plotted the residuals vs the model fitted values, while on the right the residuals (vs their indices) in a casual order.
From the right plot nothing looks wrong, however the left one (along with an R's ncvTest, p-value $1.31e-07$) may suggest heteroscedasticity at first glance, although the fact that all the data points are clustered on the left is consequence of the distribution of the upvotes response variable (hence of the fitted values). Plus I can't really spot any variance trend. I'm also reporting the plots of the residuals following the order of the predicted values:
From here a slightly more disuniform trend arises, but is it sufficient to conclude that the model is affected by heteroscedasticity?
Plus any weighted regression I've tried seems uneffective.
Thanks in advance!