  The rapid development of online media has significantly facilitated the
public's information consumption, knowledge acquisition, and opinion exchange.
However, it has also led to more violent conflicts in online discussions.
Therefore, controversy detection becomes important for computational and social
sciences. Previous research on detection methods has primarily focused on
larger datasets and more complex computational models but has rarely examined
the underlying mechanisms of conflict, particularly the psychological
motivations behind them. In this paper, we present evidence that conflicting
posts tend to have a high proportion of "ascending gradient of likes", i.e.,
replies get more likes than comments. Additionally, there is a gradient in the
number of replies between the neighboring tiers as well. We develop two new
gradient features and demonstrate the common enhancement effect of our features
in terms of controversy detection models. Further, multiple evaluation
algorithms are used to compare structural, interactive, and textual features
with the new features across multiple Chinese and English media. The results
show that it is a general case that gradient features are significantly
different in terms of controversy and are more important than other features.
More thoroughly, we discuss the mechanism by which the ascending gradient
emerges, suggesting that the case is related to the "backfire effect" in
ideological conflicts that have received recent attention. The features formed
by the psychological mechanism also show excellent detection performance in
application scenarios where only a few hot information or early information are
considered. Our findings can provide a new perspective for online conflict
behavior analysis and early detection.
