相关论文: Tweezers: A Framework for Security Event Detection…

Personal attacks in the context of social media conversations often lead to fast-paced derailment, leading to even more harmful exchanges being made. State-of-the-art systems for the detection of such conversational derailment often make…

Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g.…

In modern IT systems and computer networks, real-time and offline event log analysis is a crucial part of cyber security monitoring. In particular, event log analysis techniques are essential for the timely detection of cyber attacks and…

Social spam produces a great amount of noise on social media services such as Twitter, which reduces the signal-to-noise ratio that both end users and data mining applications observe. Existing techniques on social spam detection have…

In the era of the Internet of Things (IoT) and data sharing, users frequently upload their personal information to enterprise databases to enjoy enhanced service experiences provided by various online services. However, the widespread…

Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging that is becoming a critical source of…

This study examines the use of embedded tweets in online news media. In particular, we add to the previous literature by exploring embedded tweets across reliable and unreliable news outlets. We use a mixed-method analysis to examine how…

We present Tweet2Vec, a novel method for generating general-purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, randomly selected…

Due to their often unexpected nature, natural and man-made disasters are difficult to monitor and detect for journalists and disaster management response teams. Journalists are increasingly relying on signals from social media to detect…

Cyber threat detection has become an important area of focus in today's digital age due to the growing spread of fake information and harmful content on social media platforms such as Twitter (now 'X'). These cyber threats, often disguised…

While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this…

Social media such as Twitter provide valuable information to crisis managers and affected people during natural disasters. Machine learning can help structure and extract information from the large volume of messages shared during a crisis;…

Event detection (ED) identifies and classifies event triggers from unstructured texts, serving as a fundamental task for information extraction. Despite the remarkable progress achieved in the past several years, most research efforts focus…

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for…

Cyber Threat Intelligence (CTI) sharing is an important activity to reduce information asymmetries between attackers and defenders. However, this activity presents challenges due to the tension between data sharing and confidentiality, that…

We consider the problem of event detection based upon a (typically multivariate) data stream characterizing some system. Most of the time the system is quiescent - nothing of interest is happening - but occasionally events of interest…

Data extracted from social media platforms, such as Twitter, are both large in scale and complex in nature, since they contain both unstructured text, as well as structured data, such as time stamps and interactions between users. A key…

Due to their real time nature, microblog streams are a rich source of dynamic information, for example, about emerging events. Existing techniques for discovering such events from a microblog stream in real time (such as Twitter trending…

Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In…

This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results…