Vader: A parsimonious rule-based model for sentiment analysis of social media text

Abstract The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a goldstandard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks

An Army of Me: Sockpuppets in Online Discussion Communities

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017

User Intent Prediction in Information-seeking Conversations

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201

Salvage logging changes the taxonomic, phylogenetic and functional successional trajectories of forest bird communities

1.Salvage logging following natural disturbances may alter the natural successional trajectories of biological communities by affecting the occurrences of species, functional groups and evolutionary lineages. However, few studies have examined whether dissimilarities between bird communities of salvaged and unsalvaged forests are more pronounced for rare species, functional groups and evolutionary lineages than for their more common counterparts. 2. We compiled data on breeding bird assemblages from nine study areas in North America, Europe and Asia, covering a 17‐year period following wildfire or windstorm disturbances and subsequent salvage logging. We tested whether dissimilarities based on non‐shared species, functional groups and evolutionary lineages (a) decreased or increased over time and (b) the responses of rare, common and dominant species varied, by using a unified statistical framework based on Hill numbers and null models. 3. We found that dissimilarities between bird communities caused by salvage logging persisted over time for rare, common and dominant species, evolutionary lineages and for rare functional groups. Dissimilarities of common and dominant functional groups increased 14 years post disturbance. 4. Salvage logging led to significantly larger dissimilarities than expected by chance. Functional dissimilarities between salvaged and unsalvaged sites were lower compared to taxonomic and phylogenetic dissimilarities. In general, dissimilarities were highest for rare, followed by common and dominant species. 5. Synthesis and applications. Our research demonstrates that salvage logging did not decrease dissimilarities of bird communities over time and taxonomic, functional and phylogenetic dissimilarities persisted for over a decade. We recommend resource managers and decision makers to reserve portions of disturbed forest to enable unmanaged post‐disturbance succession of bird communities, particularly to conserve rare species found in unsalvaged disturbed forests

Salvage logging changes the taxonomic, phylogenetic and functional successional trajectories of forest bird communities

1. Salvage logging following natural disturbances may alter the natural successional trajectories of biological communities by affecting the occurrences of species, functional groups and evolutionary lineages. However, few studies have examined whether dissimilarities between bird communities of salvaged and unsalvaged forests are more pronounced for rare species, functional groups and evolutionary lineages than for their more common counterparts. 2. We compiled data on breeding bird assemblages from nine study areas in North America, Europe and Asia, covering a 17-year period following wildfire or windstorm disturbances and subsequent salvage logging. We tested whether dissimilarities based on non-shared species, functional groups and evolutionary lineages (a) decreased or increased over time and (b) the responses of rare, common and dominant species varied, by using a unified statistical framework based on Hill numbers and null models. 3. We found that dissimilarities between bird communities caused by salvage logging persisted over time for rare, common and dominant species, evolutionary lineages and for rare functional groups. Dissimilarities of common and dominant functional groups increased 14 years post disturbance. 4. Salvage logging led to significantly larger dissimilarities than expected by chance. Functional dissimilarities between salvaged and unsalvaged sites were lower compared to taxonomic and phylogenetic dissimilarities. In general, dissimilarities were highest for rare, followed by common and dominant species. 5. Synthesis and applications. Our research demonstrates that salvage logging did not decrease dissimilarities of bird communities over time and taxonomic, functional and phylogenetic dissimilarities persisted for over a decade. We recommend resource managers and decision makers to reserve portions of disturbed forest to enable unmanaged post-disturbance succession of bird communities, particularly to conserve rare species found in unsalvaged disturbed forests.Ministry of Science and Technology; Gregor Louisoder Environmental Foundatio

Food abundance does not determine bird use of early-successional habitat.

Abstract. Few attempts have been made to experimentally address the extent to which temporal or spatial variation in food availability influences avian habitat use. We used an experimental approach to investigate whether bird use differed between treated (arthropods reduced through insecticide application) and control (untreated) forest canopy gaps within a bottomland hardwood forest in the Upper Coastal Plain of South Carolina, USA. Gaps were two- to three-year-old group selection timber harvest openings of three sizes (0.13, 0.26, and 0.50 ha). Our study was conducted during four bird use periods (spring migration, breeding, post-breeding, and fall migration) in 2002 and 2003. Arthropods were reduced in treated gaps by 68% in 2002 and 73% in 2003. We used mist-netting captures and foraging attack rates to assess the influence of arthropod abundance on avian habitat use. Evidence that birds responded to arthropod abundance was limited and inconsistent. In 2002, we generally captured more birds in treated gaps of the smallest size (0.13 ha) and fewer birds in treated gaps of the larger sizes. In 2003, we recorded few differences in the number of captures in treated and control gaps. Foraging attack rates generally were lower in treated than in control gaps, indicating that birds were able to adapt to the reduced food availability and remain in treated gaps. We conclude that arthropod abundance was not a proximate factor controlling whether forest birds used our gaps. The abundance of food resources may not be as important in determining avian habitat selection as previous research has indicated, at least for passerines in temperate subtropical regions

Recognizing thyrotoxicosis in a patient with bipolar mania: a case report

<p>Abstract</p> <p>Background</p> <p>A thyroid stimulating hormone level is commonly measured in patients presenting with symptoms of mania in order to rule out an underlying general medical condition such as hyperthyroidism or thyrotoxicosis. Indeed, many cases have been reported in which a patient is initially treated for bipolar mania, but is later found to have a thyroid condition. Several case reports have noted the development of a thyroid condition in bipolar patients either on lithium maintenance treatment or recently on lithium treatment.</p> <p>Case presentation</p> <p>We review a case in which a patient with a long history of bipolar disorder presents with comorbid hyperthyroidism and bipolar mania after recent discontinuation of lithium treatment.</p> <p>Conclusion</p> <p>Physicians should consider a comorbid hyperthyroidism in bipolar manic patients only partially responsive to standard care treatment with a mood stabilizer and antipsychotic.</p

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

Climatic patterns in the establishment of wintering areas by North American migratory birds

Long-distance migration in birds is relatively well studied in nature; however, one aspect of this phenomenon that remains poorly understood is the pattern of distribution presented by species during arrival to and establishment of wintering areas. Some studies suggest that the selection of areas in winter is somehow determined by climate, given its influence on both the distribution of bird species and their resources. We analyzed whether different migrant passerine species of North America present climatic preferences during arrival to and departure from their wintering areas. We used ecological niche modeling to generate monthly potential climatic distributions for 13 migratory bird species during the winter season by combining the locations recorded per month with four environmental layers. We calculated monthly coefficients of climate variation and then compared two GLM (generalized linear models), evaluated with the AIC (Akaike information criterion), to describe how these coefficients varied over the course of the season, as a measure of the patterns of establishment in the wintering areas. For 11 species, the sites show nonlinear patterns of variation in climatic preferences, with low coefficients of variation at the beginning and end of the season and higher values found in the intermediate months. The remaining two species analyzed showed a different climatic pattern of selective establishment of wintering areas, probably due to taxonomic discrepancy, which would affect their modeled winter distribution. Patterns of establishment of wintering areas in the species showed a climatic preference at the macroscale, suggesting that individuals of several species actively select wintering areas that meet specific climatic conditions. This probably gives them an advantage over the winter and during the return to breeding areas. As these areas become full of migrants, alternative suboptimal sites are occupied. Nonrandom winter area selection may also have consequences for the conservation of migratory bird species, particularly under a scenario of climate change