Publications

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Conference Proceedings
Guntuku, Sharath Chandra, Weisi Lin, Jordan Carpenter, Wee Keong Ng, Lyle Ungar, and Daniel Preotiuc-Pietro. Studying Personality through the Content of Posted and Liked Images on Twitter In Web Science., 2017. AbstractPDFSlides

Interacting with images through social media has become widespread due to ubiquitous Internet access and multimedia enabled devices. Through images, users generally present their daily activities, preferences or interests. This study aims to identify the way and extent to which personality differences measured as using the Big Five model are related to online image posting and liking. In two experiments, the larger consisting of ~$1.5 million Twitter images both posted and liked by ~4,000 users, we extract interpretable semantic concepts using large-scale image content analysis and analyze differences specific of each personality trait. Predictive results show that image content can predict personality traits, and that there can be significant performance gain by fusing the signal from both posted and liked images.

Preoţiuc-Pietro, Daniel, Jordan Carpenter, Salvatore Giorgi, and Lyle Ungar. Studying the Dark Triad of Personality using Twitter Behavior. CIKM., 2016. AbstractPDF

Research into the darker traits of human nature is growing in interest especially in the context of increased social media usage. This allows users to express themselves to a wider online audience. We study the extent to which the standard model of dark personality – the dark triad – consisting of narcissism, psychopathy and Machiavellianism, is related to observable Twitter behavior such as platform usage, posted text and profile image choice. Our results show that we can map various behaviors to psychological theory and study new aspects related to social media usage. Finally, we build a machine learning algorithm that predicts the dark triad of personality in out-of-sample users with reliable accuracy.

Preoţiuc-Pietro, Daniel, Srijith P.K., Mark Hepple, and Trevor Cohn. Studying the temporal dynamics of word co-occurrences: An application to event detection In LREC., 2016. AbstractPDFSlides

Streaming media provides a number of unique challenges for computational linguistics. This paper studies the temporal variation in word co-occurrence statistics, with application to event detection. We develop a spectral clustering approach to find groups of mutually informative terms occurring in discrete time frames. Experiments on large datasets of tweets show that these groups identify key real world events as they occur in time, despite no explicit supervision. The performance of our method rivals state-of-the-art methods for event detection on F-score, obtaining higher recall at the expense of precision.

Journal Article
Preoţiuc-Pietro, Daniel, Svitlana Volkova, Vasileios Lampos, Yoram Bachrach, and Nikolaos Aletras. "Studying User Income through Language, Behaviour and Affect in Social Media." PLoS ONE 10 (2015). AbstractWebsite

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.

Srijith, PK, Kalina Bontcheva, Mark Hepple, and Daniel Preoţiuc-Pietro. "Sub-Story Detection in Twitter with Hierarchical Dirichlet Processes." Information Processing and Management (2016). AbstractPDFWebsite

Social media has now become the de facto information source on real world events. The challenge, however, due to the high volume and velocity nature of social media streams, is in how to follow all posts pertaining to a given event over time – a task referred to as story detection. Moreover, there are often several different stories pertaining to a given event, which we refer to as sub-stories and the corresponding task of their automatic detection – as sub-story detection. This paper proposes hierarchical Dirichlet processes (HDP), a probabilistic topic model, as an effective method for automatic sub-story detection. HDP can learn sub-topics associated with sub-stories which enables it to handle subtle variations in sub-stories. It is compared with state-of-the-art story detection approaches based on locality sensitive hashing and spectral clustering. We demonstrate the superior performance of HDP for sub-story detection on real world Twitter data sets using various evaluation measures. The ability of HDP to learn sub-topics helps it to recall the sub-stories with high precision. This has resulted in an improvement of up to 60% in the F-score performance of HDP based sub-story detection approach compared to standard story detection approaches. A similar performance improvement is also seen using an information theoretic evaluation measure proposed for the sub-story detection task. Another contribution of this paper is in demonstrating that considering the conversational structures within the Twitter stream can bring up to 200% improvement in sub-story detection performance.

Dinca, Eduard, John Yianni, Jeremy Rowe, Matthias Radatz, Daniel Preoţiuc-Pietro, Paul Rundle, Ian Rennie, and Andras Kemeny. "Survival and complications following Gamma Knife radiosurgery or enucleation for ocular melanoma: a 20-year experience." Acta Neurochirurgica 154 (2012): 605-610. AbstractWebsite

Background

We present our experience in treating ocular melanoma at the National Centre for Stereotactic Radiosurgery in Sheffield, UK over the last 20 years.

Method

We analysed 170 patients treated with Gamma Knife radiosurgery, recorded the evolution of visual acuity and complication rates, and compared their survival with 620 patients treated with eye enucleation. Different peripheral doses (using the 50% therapeutic isodose) were employed: 50-70 Gy for 24 patients, 45 Gy for 71 patients, 35 Gy for 62 patients.

Findings

There was no significant difference in survival between the 35-Gy, 45-Gy and 50– to 70-Gy groups when compared between themselves (p = 0.168) and with the enucleation group (p = 0.454). The 5-year survival rates were: 64% for 35 Gy, 62.71% for 45 Gy, 63.6% for 50–70 Gy and 65.2% for enucleated patients. Clinical variables influencing survival for radiosurgery patients were tumour volume (p = 0.014) and location (median 66.4 vs 37.36 months for juxtapapillary vs peripheral tumours, respectively; p = 0.001), while age and gender did not prove significant. Regarding complications, using 35 Gy led to more than a 50% decrease, when compared with the 45-Gy dose, in the incidence of cataract, glaucoma and retinal detachment. Retinopathy, optic neuropathy and vitreous haemorrhage were not significantly influenced. Blindness decreased dramatically from 83.7% for 45 Gy to 31.4% for 35 Gy (p = 0.006), as well as post-radiosurgery enucleation: 23.9% for 45 Gy vs 6.45% for 35 Gy (p = 0.018). Visual acuity, recorded up to 5 years post-radiosurgery, was significantly better preserved for 35 Gy than for 45 Gy (p = 0.0003).

Conclusions
Using 35 Gy led to a dramatic decrease in complications, vision loss and salvage enucleation, while not compromising patient survival.