With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.

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Place name extraction refers to the task of detecting precise location information in texts like microblogs. It is a vital task to assist disaster response, revealing where the damages are, where people need assistance, and where help can be found. All current approaches for extracting the place names from microblogs face crucial problems: rule-based methods do not generalize, gazetteer-based methods do not detect unknown multi-word place names, and machine learning methods lack sufficient data, which is costly to annotate on scale. We propose a hybrid method that avoids these problems, named GazPNE, which fuses rules, gazetteers, and deep learning methods to achieve state-of-the-art-performance without requiring any manually annotated data.Specifically, we utilize C-LSTM, a fusion of Convolutional and Long Short-Term Memory Neural Networks, to decide if an n-gram in a microblog text is a place name or not. The C-LSTM is trained on 4.6 million positive examples extracted from OpenStreetMap and GeoNames and 220 million negative examples synthesized by rules and evaluated on 4,500 disaster-related tweets, including 9,026 place names from three floods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). Our method improves the previous state-of-the-art by 6%, achieving an F1 of 0.86.

Imprecise composite location references formed using ad hoc spatial expressions in English text makes the geocoding task challenging for both inference and evaluation. Typically such spatial expressions fill in unestablished areas with new toponyms for finer spatial referents. For example, the spatial extent of the ad hoc spatial expression "north of" or "50 minutes away from" in relation to the toponym "Dayton, OH" refers to an ambiguous, imprecise area, requiring translation from this qualitative representation to a quantitative one with precise semantics using systems such as WGS84. Here we highlight the challenges of geocoding such referents and propose a formal representation that employs background knowledge, semantic approximations and rules, and fuzzy linguistic variables. We also discuss an appropriate evaluation technique for the task that is based on human contextualized and subjective judgment. CCS CONCEPTS• Information systems → Evaluation of retrieval results; Geographic information systems;

Extracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names. Variability, particularly systematic variability in location names (Carroll, 1983), challenges the identification task. Some of this variability can be anticipated as operations within a statistical language model, in this case drawn from gazetteers such as OpenStreetMap (OSM), Geonames, and DBpedia. This permits evaluation of an observed n-gram in Twitter targeted text as a legitimate location name variant from the same location-context. Using n-gram statistics and location-related dictionaries, our Location Name Extraction tool (LNEx) handles abbreviations and automatically filters and augments the location names in gazetteers (handling name contractions and auxiliary contents) to help detect the boundaries of multi-word location names and thereby delimit them in texts.We evaluated our approach on 4,500 event-specific tweets from three targeted streams to compare the performance of LNEx against that of ten state-of-the-art taggers that rely on standard semantic, syntactic and/or orthographic features. LNEx improved the average F-Score by 33-179%, outperforming all taggers. Further, LNEx is capable of stream processing. 1 This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/ 1 Data and the tool is available at https://github.com/halolimat/LNEx 2 We define a targeted stream as a set of tweets that has the potential to satisfy an event-related information need (Piskorski and Ehrmann, 2013) crawled using keywords and hashtags, to contextualize the event (e.g., "#HarveyFlood").