Using word and phrase abbreviation patterns to extract age from Twitter microtexts

The wealth of texts available publicly online for analysis is ever increasing. Much work in computational linguistics focuses on syntactic, contextual, morphological and phonetic analysis on written documents, vocal recordings, or texts on the internet. Twitter messages present a unique challenge for computational linguistic analysis due to their constrained size. The constraint of 140 characters often prompts users to abbreviate words and phrases. Additionally, as an informal writing medium, messages are not expected to adhere to grammatically or orthographically standard English. As such, Twitter messages are noisy and do not necessarily conform to standard writing conventions of linguistic corpora, often requiring special pre-processing before advanced analysis can be done. In the area of computational linguistics, there is an interest in determining latent attributes of an author. Attributes such as author gender can be determined with some amount of success from many sources, using various methods, such as analysis of shallow linguistic patterns or topic. Author age is more difficult to determine, but previous research has been somewhat successful at classifying age as a binary (e.g. over or under 30), ternary, or even as a continuous variable using various techniques. Twitter messages present a difficult problem for latent user attribute analysis, due to the pre-processing necessary for many computational linguistics analysis tasks. An added logistical challenge is that very few latent attributes are explicitly defined by users on Twitter. Twitter messages are a part of an enormous data set, but the data set must be independently annotated for latent writer attributes not defined through the Twitter API before any classification on such attributes can be done. The actual classification problem is another particular challenge due to restrictions on tweet length. Previous work has shown that word and phrase abbreviation patterns used on Twitter can be indicative of some latent user attributes, such as geographic region or the Twitter client (iPhone, Android, Twitter website, etc.) used to make posts. Language change has generally been posited as being driven by women. This study explores if there there are age-related patterns or change in those patterns over time evident in Twitter posts from a variety of English authors. This work presents a growable data set annotated by Twitter users themselves for age and other useful attributes. The study also presents an extension of prior work on Twitter abbreviation patterns which shows that word and phrase abbreviation patterns can be used toward determining user age. Notable results include classification accuracy of up to 83%, which was 63% above relative majority class baseline (ZeroR in Weka) when classifying user ages into 6 equally sized age bins using a multilayer perceptron network classifier

Mining the Demographics of Political Sentiment from Twitter Using Learning from Label Proportions

Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning from Label Proportions (LLP) models for demographic and opinion inference using U.S. Census, national and state political polls, and Cook partisan voting index as population level data. In LLP classification settings, the training data is divided into a set of unlabeled bags, where only the label distribution in of each bag is known, removing the requirement of instance-level annotations. Our proposed LLP model, Weighted Label Regularization (WLR), provides a scalable generalization of prior work on label regularization to support weights for samples inside bags, which is applicable in this setting where bags are arranged hierarchically (e.g., county-level bags are nested inside of state-level bags). We apply our model to Twitter data collected in the year leading up to the 2016 U.S. presidential election, producing estimates of the relationships among political sentiment and demographics over time and place. We find that our approach closely tracks traditional polling data stratified by demographic category, resulting in error reductions of 28-44% over baseline approaches. We also provide descriptive evaluations showing how the model may be used to estimate interactions among many variables and to identify linguistic temporal variation, capabilities which are typically not feasible using traditional polling methods

An analysis of the user occupational class through Twitter content

Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications

Identifying communicator roles in Twitter

Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge

Listening between the Lines: Learning Personal Attributes from Conversations

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.Comment: published in WWW'1