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Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.

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.

Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court’s decisions with a strong accuracy (79% on average). Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis.

Online, social media communication is often ambiguous, and it can encourage speed and inattentiveness. We investigated whether Actively Open Minded Thinking (AOT), a dispositional willingness to seek out new or potentially threatening information, may help users avoid these pitfalls. In Study 1, we determined that correctly assessing social media authors' traits was positively predicted by raters' AOT. In Study 2, we used data-driven methods to devise a three-dimensional picture of online behaviors of people high or low in AOT, finding that AOT is associated with thoughtful, nuanced, idiosyncratic actions and with resisting the typically fast pace of online interactions. AOT may be an important factor in accurate, socially responsible online behavior.

Assessing the predictive value of different social media platforms is important to understand the variation in how users reveal themselves across multiple platforms. Most social media platforms allow users to interact in multiple ways: by posting content to the platform, liking others' posts, or building a user profile. While prior studies offer insights into how language use differs across platforms, differences in image usage is less well understood. In this study, we analyzed variation in image content with user personality across three interaction types (posts, likes and profile images) and two platforms, using a unique data set of users who are active on both Twitter and Flickr. Usage patterns on these two social media platforms revealed different aspects of users' personality. Cross-platform data fusion is thus shown to improve personality prediction performance.

The present paper concentrates on the issue of feature selection for unsupervised word sense disambiguation (WSD) performed with an underlying Naïve Bayes model. It introduces web N-gram features which, to our knowledge, are used for the first time in unsupervised WSD. While creating features from unlabeled data, we are “helping” a simple, basic knowledge-lean disambiguation algorithm to significantly increase its accuracy as a result of receiving easily obtainable knowledge. The performance of this method is compared to that of others that rely on completely different feature sets. Test results concerning nouns, adjectives and verbs show that web N-gram feature selection is a reliable alternative to previously existing approaches, provided that a “quality list” of features, adapted to the part of speech, is used.

Online, social media communication is often ambiguous, and it can encourage speed and inattentiveness. We investigated whether Actively Open Minded Thinking (AOT), a dispositional willingness to seek out new or potentially threatening information, may help users avoid these pitfalls. In Study 1, we determined that correctly assessing social media authors’ traits was positively predicted by raters’ AOT. In Study 2, we used data-driven methods to devise a three-dimensional picture of online behaviors of people high or low in AOT, finding that AOT is associated with thoughtful, nuanced, idiosyncratic actions and with resisting the typically fast pace of online interactions. AOT may be an important factor in accurate, socially responsible online behavior.

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.

Identifying risks associated with a company is important to investors and the well-being of the overall financial market. In this study, we build a computational framework to automatically extract company risk factors from news articles. Our newly proposed schema comprises seven distinct aspects, such as supply chain, regulations, and competitions. We sample and annotate 744 news articles and benchmark various machine learning models. While large language models have achieved huge progress in various types of NLP tasks, our experiment shows that zero-shot and few-shot prompting state-of-the-art LLMs (e.g. LLaMA-2) can only achieve moderate to low performances in identifying risk factors. And fine-tuned pre-trained language models are performing better on most of the risk factors. Using this model, we analyze over 277K Bloomberg news articles and demonstrate that identifying risk factors from news could provide extensive insight into the operations of companies and industries.

There are significant temporal dependencies between online behaviour and occurring real world activities. Particularly in text modelling, these are usually ignored or at best dealt with in overly simplistic ways such as assuming smooth variation with time. Social media is a new data source which present collective behaviour much more richly than traditional sources, such as newswire, with a finer time granularity, timely reflection of activities, multiple modalities and large volume. Analysing temporal patterns in this data is important in order to discover newly emerging topics, periodic occurrences and correlation or causality to real world indicators or human behaviour patterns. With these opportunities come many challenges, both engineering (i.e.\\ data volume and processing) and algorithmic, namely the inconsistency and short length of the messages and the presence of large amounts of irrelevant messages to our goal. Equipped with a better understanding of the dynamics of the complex temporal dependencies, tasks such as classification can be augmented to provide temporally aware responses. In this thesis we model the temporal dynamics of social media data. We first show that temporality is an important characteristic of this type of data. Further comparisons and correlation to real world indicators show that this data gives a timely reflection of real world events. Our goal is to use these variations to discover emerging or recurring user behaviours. We consider both the use of words and user behaviour in social media. With these goals in mind, we adapt existing and build novel machine learning techniques. These span a wide range of models: from Markov models to regularised regression models and from evolutionary spectral clustering which models smooth temporal variation to Gaussian Process regression which can identify more complex temporal patterns. We introduce approaches which discover and predict words, topics or behaviours that change over time or occur with some regularity. These are modeled for the first time in the NLP literature by using Gaussian Processes. We demonstrate that we can effectively pick out patterns, including periodicities, and achieve state-of-the-art forecasting results. We show that this performance gain transfers to improve tasks which do not take temporal information in account. Further analysed is how temporal variation in the text can be used to discover and track new content. We develop a model that exploits the variation in word co-occurrences for clustering over time. Different collection and processing tools, as well as several datasets of social media data have been developed and published as open-source software. The thesis posits that temporal analysis of data, from social media in particular, provides us with insights into real-world dynamics. Incorporating this temporal information into other applications can benefit standard tasks in natural language processing and beyond.