As the volume and complexity of forced movement continues to grow, there is an urgent need to use new data sources to better understand emerging crises. Organic sources, like social media and newspapers, can offer insights in near real time when administrative data are unavailable for timely and detailed analysis. However, in order to flexibly switch to different contexts, we need the ability to contextualize the drivers of movement for different locations and languages. Recent advances in natural language processing and specifically, neural machine translation, have shown impressive results on standard benchmark datasets for well-studied language pairs. However, the effectiveness of these models in a real-world scenario remains less known. To advance our understanding of real-world, contextual translation, we systematically study the performance of multiple widely used off-the-shelf machine translation tools using words associated with drivers of forced movement in both high- and low-resource languages. Our empirical results suggest significant variation between the performance of these machine translation tools in terms of accuracy and efficiency, highlighting a problem that must be faced by those conducting migration research using multilingual contexts. We conclude by suggesting strategies for obtaining reasonable translations from off-the-shelf language tools.

Trajectory user linking (TUL) is a problem in trajectory classification that links anonymous trajectories to the users who generated them. TUL has various uses such as identity verification, personalized recommendation, epidemiological monitoring, and threat assessments. A major challenge in TUL modeling is sparse data. Previous TUL research heavily relies on sequence-to-sequence models such as RNNs and LSTMs, with trajectory segmentation to combat sparsity, but segmentation does not sufficiently address the issue and existing models often ignore data skewness, resulting in poor precision and performance. To address these problems, we present TULHOR, a TUL model inspired by BERT, a popular language representation model. One of TULHOR’s innovations is the use of higher-order mobility flow data representations enabled by geographic area tessellation. This allows the model to alleviate the sparsity problem and also to generalize better. TULHOR consists of a spatial embedding layer, a spatial-temporal embedding layer and an encoder layer, which encodes properties and learns a rich trajectory representation. It is trained in two steps, first using a masked language modeling task to learn general embeddings, then fine-tuned using a balanced cross-entropy loss to make predictions while handling imbalanced data. Experiments on real-life mobility data show TULHOR’s effectiveness as compared to current state-of-the-art models.

One popular thread of research in computational sarcasm detection involves modeling sarcasm as a contrast between positive and negative sentiment polarities or exploring more fine-grained categories of emotions such as happiness, sadness, surprise, and so on. Most current models, however, treat these affective features independently, without regard for the sequential information encoded among the affective states. In order to explore the role of transitions in affective states, we formulate the task of sarcasm detection as a sequence classification problem by leveraging the natural shifts in various emotions over the course of a piece of text. Experiments conducted on datasets from two different genres suggest that our proposed approach particularly benefits datasets with limited labeled data and longer instances of text.

Sarcasm detection from text has gained increasing attention. While one thread of research has emphasized the importance of affective content in sarcasm detection, another avenue of research has explored the effectiveness of word representations. In this paper, we introduce a novel model for automated sarcasm detection in text, called Affective Word Embeddings for Sarcasm (AWES), which incorporates affective information into word representations. Extensive evaluation on sarcasm detection on six datasets across three domains of text (tweets, reviews and forum posts) demonstrates the effectiveness of the proposed model. The experimental results indicate that while sentiment affective representations yield best results on datasets comprising of short length text such as tweets, richer representations derived from fine-grained emotions are more suitable for detecting sarcasm from longer length documents such as product reviews and discussion forum posts.

Forced migration is increasingly becoming a global issue of concern. In this paper, we present an effective model of targeted event detection, as an essential step towards the forced migration detection problem. To date, most of the the approaches deal with the event detection in a general setting with the main objective of detecting the presence or onset of an event. However, we focus on analyzing the magnitude of a given event from a collection of text documents such as news articles from multiple sources. We use violence as an illustration as it is one of the most critical factors of forced migration. The recent advancements in semantic similarity measures are adopted to obtain relevant violence scores for each word in the vocabulary of news articles in an unsupervised manner. The resulting scores are then used to compute the average daily violence scores over a period of three months. Evaluation of the proposed model against a manually annotated data set yields a Pearson's correlation of 0.8. We also include a case study exploring the relationship between violence and key events.

Emotion detection from text is a relatively new classification task. This paper proposes a novel unsupervised context-based approach to detecting emotion from text at the sentence level. The proposed methodology does not depend on any existing manually crafted affect lexicons such as Word Net-Affect, thereby rendering our model flexible enough to classify sentences beyond Ekman's model of six basic emotions. Our method computes an emotion vector for each potential affect bearing word based on the semantic relatedness between words and various emotion concepts. The scores are then fine tuned using the syntactic dependencies within the sentence structure. Extensive evaluation on various data sets shows that our framework is a more generic and practical solution to the emotion classification problem and yields significantly more accurate results than recent unsupervised approaches.