Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Event detection from tets is an important task to understand the current events/topics attracting a large number of common users. Hover, the unique characteristics of tets (e.g. short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. Most existing techniques proposed for ll written documents (e.g. news articles) cannot be directly adopted. ,  propose a segment-based event detection system for tets, called Tvent. Tvent first detects bursty tet segments as event segments and then clusters the event segments into events considering both their frequency distribution and content similarity. More specifically, each tet is split into non-overlapping segments (i.e. phrases possibly refer to named entities or semantically meaningful information units). The bursty segments are identified within a fixed time window based on their frequency patterns, and each bursty segment is described by the set of tets containing the segment published within that time window. The similarity beten a pair of bursty segments is computed using their associated tets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified events.  evaluate Tvent and compare it with the state-of-the-art method using 4.3 million tets published by Singapore-based users in June 2010. In our experiments, Tvent outperforms the state-of-the-art method by a large margin in terms of both precision and recall. More importantly, the events detected by Tvent can be easily interpreted with little background knowledge because of the newsworthy segments.  also show that Tvent is efficient and scalable, leading to a desirable solution for event detection from tets.
['Chenliang Li', 'Aixin Sun', 'Anwitaman Datta']
I am writing a research paper on a idea Twevent: segment-based event detection from tweets
[{"from": "human", "value": "I am writing a research paper on a idea Twevent: segment-based event detection from tweets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Event detection from tets is an important task to understand the current events/topics attracting a large number of common users. Hover, the unique characteristics of tets (e.g. short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. Most existing techniques proposed for ll written documents (e.g. news articles) cannot be directly adopted. ,  propose a segment-based event detection system for tets, called Tvent. Tvent first detects bursty tet segments as event segments and then clusters the event segments into events considering both their frequency distribution and content similarity. More specifically, each tet is split into non-overlapping segments (i.e. phrases possibly refer to named entities or semantically meaningful information units). The bursty segments are identified within a fixed time window based on their frequency patterns, and each bursty segment is described by the set of tets containing the segment published within that time window. The similarity beten a pair of bursty segments is computed using their associated tets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified events.  evaluate Tvent and compare it with the state-of-the-art method using 4.3 million tets published by Singapore-based users in June 2010. In our experiments, Tvent outperforms the state-of-the-art method by a large margin in terms of both precision and recall. More importantly, the events detected by Tvent can be easily interpreted with little background knowledge because of the newsworthy segments.  also show that Tvent is efficient and scalable, leading to a desirable solution for event detection from tets."}]