Abstract / Description of output
Curbing hate speech is undoubtedly a major challenge for online
microblogging platforms like Twitter. While there have been studies
around hate speech detection, it is not clear how hate speech finds
its way into an online discussion. It is important for a content
moderator to not only identify which tweet is hateful, but also
to predict which tweet will be responsible for accumulating hate
speech. This would help in prioritizing tweets that need constant
monitoring. Our analysis reveals that for hate speech to manifest
in an ongoing discussion, the source tweet may not necessarily be
hateful; rather, there are plenty of such non-hateful tweets which
gradually invoke hateful replies, resulting in the entire reply threads
becoming provocative.
In this paper,we define a novel problem – given a source tweet and
a few of its initial replies, the task is to forecast the hate intensity of
upcoming replies. To this end, we curate a novel dataset constituting
∼ 4.5𝑘 contemporary tweets and their entire reply threads. Our preliminary
analysis confirms that the evolution patterns along time
of hate intensity among reply threads have highly diverse patterns,
and there is no significant correlation between the hate intensity of
the source tweets and that of their reply threads. We employ seven
state-of-the-art dynamic models (either statistical signal processing
or deep learning based) and show that they fail badly to forecast the
hate intensity. We then propose DESSERT, a novel deep state-space
model that leverages the function approximation capability of deep
neural networks with the capacity to quantify the uncertainty of
statistical signal processing models. Exhaustive experiments and
ablation study show that DESSERT outperforms all the baselines
substantially. Further, its deployment in an advanced AI platform
designed to monitor real-world problematic hateful content has improved
the aggregated insights extracted for countering the spread
of online harms.
microblogging platforms like Twitter. While there have been studies
around hate speech detection, it is not clear how hate speech finds
its way into an online discussion. It is important for a content
moderator to not only identify which tweet is hateful, but also
to predict which tweet will be responsible for accumulating hate
speech. This would help in prioritizing tweets that need constant
monitoring. Our analysis reveals that for hate speech to manifest
in an ongoing discussion, the source tweet may not necessarily be
hateful; rather, there are plenty of such non-hateful tweets which
gradually invoke hateful replies, resulting in the entire reply threads
becoming provocative.
In this paper,we define a novel problem – given a source tweet and
a few of its initial replies, the task is to forecast the hate intensity of
upcoming replies. To this end, we curate a novel dataset constituting
∼ 4.5𝑘 contemporary tweets and their entire reply threads. Our preliminary
analysis confirms that the evolution patterns along time
of hate intensity among reply threads have highly diverse patterns,
and there is no significant correlation between the hate intensity of
the source tweets and that of their reply threads. We employ seven
state-of-the-art dynamic models (either statistical signal processing
or deep learning based) and show that they fail badly to forecast the
hate intensity. We then propose DESSERT, a novel deep state-space
model that leverages the function approximation capability of deep
neural networks with the capacity to quantify the uncertainty of
statistical signal processing models. Exhaustive experiments and
ablation study show that DESSERT outperforms all the baselines
substantially. Further, its deployment in an advanced AI platform
designed to monitor real-world problematic hateful content has improved
the aggregated insights extracted for countering the spread
of online harms.
| Original language | English |
|---|---|
| Pages | 2732-2742 |
| Number of pages | 11 |
| DOIs | |
| Publication status | Published - 14 Aug 2021 |
| Event | Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021 - Duration: 14 Aug 2021 → 14 Aug 2021 |
Conference
| Conference | Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021 |
|---|---|
| Period | 14/08/21 → 14/08/21 |