108 citations · 231 across the 9 of their papers we have counts for
10 papers
Analyzing social media with crowdsourcing in Crowd4SDG
Carlo Bono, Mehmet Oğuz Mülâyim, Cinzia Cappiello +6
Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among millions of posts being post…
Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter
Virginia Negri, Dario Scuratti, Stefano Agresti +6
Social Media provides a trove of information that, if aggregated and analysed appropriately can provide important statistical indicators to policy makers. In some situations these…
SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations
Marian-Andrei Rizoiu, Swapnil Mishra, Quyu Kong +2
Among the statistical tools for online information diffusion modeling, both epidemic models and Hawkes point processes are popular choices. The former originate from epidemiology,…
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
Aditya Joshi, Samarth Agrawal, Pushpak Bhattacharyya +1
The trigram `I love being' is expected to be followed by positive words such as `happy'. In a sarcastic sentence, however, the word `ignored' may be observed. The expected and the…
`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection
Aditya Joshi, Prayas Jain, Pushpak Bhattacharyya +1
Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports a simple topic model for sarcasm detection, a first, to the best of our kno…
Are Word Embedding-based Features Useful for Sarcasm Detection?
Aditya Joshi, Vaibhav Tripathi, Kevin Patel +2
This paper makes a simple increment to state-of-the-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at t…
A Computational Approach to Automatic Prediction of Drunk Texting
Aditya Joshi, Abhijit Mishra, Balamurali AR +2
Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a t…
Automatic Identification of Sarcasm Target: An Introductory Approach
Aditya Joshi, Pranav Goel, Pushpak Bhattacharyya +1
Past work in computational sarcasm deals primarily with sarcasm detection. In this paper, we introduce a novel, related problem: sarcasm target identification i.e., extracting the…
Automatic Sarcasm Detection: A Survey
Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman
Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-b…
Deep Broad Learning - Big Models for Big Data
Nayyar A. Zaidi, Geoffrey I. Webb, Mark J. Carman +1
Deep learning has demonstrated the power of detailed modeling of complex high-order (multivariate) interactions in data. For some learning tasks there is power in learning models t…