Text Streams Research Articles

StanceVis Prime: visual analysis of sentiment and stance in social media texts

Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert.Graphic abstract

τ-SS3: A text classifier with dynamic n-grams for early risk detection over text streams

A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF’s eRisk open tasks. SS3 was designed to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important “on the fly”. This paper introduces τ-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that τ-SS3 is able to improve both current results and the richness of visual explanations.

A Survey on Event Detection Models for Text Data Streams

Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

Real-Time Tweet Analytics Using Hybrid Hashtags on Twitter Big Data Streams

Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolving nature, lack of context, slang, abbreviations, and non-standardized expression by users. Most existing approaches, which utilize hashtags for tweet topic classification, focus on extracting hashtag concepts from external lexicon resources to derive semantics. However, due to the rapid evolution and non-standardized expression of hashtags, the majority of these lexicon resources either suffer from the lack of hashtag words in their knowledge bases or use multiple resources at once to derive semantics, which make them unscalable. Along with scalable and automated techniques for tweet topic classification using hashtags, there is also a requirement for real-time analytics approaches to handle huge and dynamic flows of textual streams generated by Twitter. To address these problems, this paper first presents a novel semi-automated technique that derives semantically relevant hashtags using a domain-specific knowledge base of topic concepts and combines them with the existing tweet-based-hashtags to produce Hybrid Hashtags. Further, to deal with the speed and volume of Big Data streams of tweets, we present an online approach that updates the preprocessing and learning model incrementally in a real-time streaming environment using the distributed framework, Apache Storm. Finally, to fully exploit the batch and stream environment performance advantages, we propose a comprehensive framework (Hybrid Hashtag-based Tweet topic classification (HHTC) framework) that combines batch and online mechanisms in the most effective way. Extensive experimental evaluations on a large volume of Twitter data show that the batch and online mechanisms, along with their combination in the proposed framework, are scalable, efficient, and provide effective tweet topic classification using hashtags.

Story Forest

Extracting events accurately from vast news corpora and organize events logically is critical for news apps and search engines, which aim to organize news information collected from the Internet and present it to users in the most sensible forms. Intuitively speaking, an event is a group of news documents that report the same news incident possibly in different ways. In this article, we describe our experience of implementing a news content organization system at Tencent to discover events from vast streams of breaking news and to evolve news story structures in an online fashion. Our real-world system faces unique challenges in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we (1) need to accurately and quickly extract distinguishable events from massive streams of long text documents, and (2) must develop the structures of event stories in an online manner, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest , a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. A core novelty of our Story Forest system is EventX , a semi-supervised scheme to extract events from massive Internet news corpora. EventX relies on a two-layered, graph-based clustering procedure to group documents into fine-grained events. We conducted extensive evaluations based on (1) 60 GB of real-world Chinese news data, (2) a large Chinese Internet news dataset that contains 11,748 news articles with truth event labels, and (3) the 20 News Groups English dataset, through detailed pilot user experience studies. The results demonstrate the superior capabilities of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers.

Event detection and evolution in multi-lingual social streams

Real-life events are emerging and evolving in social and news streams. Recent methods have succeeded in capturing designed features of monolingual events, but lack of interpretability and multi-lingual considerations. To this end, we propose a multi-lingual event mining model, namely MLEM, to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English, Chinese, French, German, Russian and Japanese. Specially, we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec model. We propose an 8-tuple to describe event for correlation analysis and evolution graph generation. We evaluate the MLEM model using a massive human-generated dataset containing real world events. Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.

A Multi-Stream Recurrent Neural Network for Social Role Detection in Multiparty Interactions

Understanding multiparty human interaction dynamics is a challenging problem involving multiple data modalities and complex ordered interactions between multiple people. We propose a unified framework that integrates synchronized video, audio, and text streams from four people to capture the interaction dynamics in natural group meetings. We focus on estimating the dynamic social role of the meeting participants, i.e., Protagonist, Neutral, Supporter, or Gatekeeper. Our key innovation is to incorporate both co-occurrence features and successive occurrence features in thin time windows to better describe the behavior of a target participant and his/her responses from others, using a multi-stream recurrent neural network. We evaluate our algorithm on the widely-used AMI corpus and achieve state-of-the-art accuracy of 78% for automatic dynamic social role detection. We further investigate the importance of different video and audio features for estimating social roles.

Підхід до виявлення аномалій в потоках тектових даних

Збільшення потоків інформації веде за собою необхідність розробки спеціалізованих інтелектуальних засобів та методів для обробки величезної кількості даних. Так популярність соціальних мереж, різного роду месенджерів вимагає створення спеціалізованих засобів для обробки потоку текстових повідомлень. Дане дослідження присвячене дослідженню та розробці методу виявлення аномальних елементів в потоках текстових даних. Особлива увага присвячена підтримці україномовних текстів.

A Dirichlet process biterm-based mixture model for short text stream clustering

Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics.

ViSSa: Recognizing the appropriateness of videos on social media with on-demand crowdsourcing

The recent significant growth of social media has brought the attention of researchers toward monitoring the enormous amount of streaming data using real-time approaches. This data may appear in different forms like streaming text, images, audio, videos, etc. In this paper, we address the problem of deciding the appropriateness of streaming videos with the help of on-demand crowdsourcing. We propose a novel crowd-powered model ViSSa, which is an open crowdsourcing platform that helps to automatically detect appropriateness of the videos getting uploaded online through employing the viewers of existing videos. The proposed model presents a unique approach of not only identifying unsafe videos but also detecting the portion of inappropriateness (in terms of platform’s vulnerabilities). Our experiments with 47 crowd contributors demonstrate the effectiveness of the proposed approach. On the designed ViSSa platform, 18 safe videos are initially posted. After getting access, 20 new videos are added by different users. These videos are assessed (and marked as safe or unsafe) by users and finally with judgment analysis a consensus judgment is obtained. The approach detects the unsafe videos with high accuracy (95%) and point out the portion of inappropriateness. Interestingly, changing the mode of video segment allocation (homogeneous and heterogeneous) is found to have a significant impact on the viewers’ feedback. However, the proposed approach performs consistently well in different modes of viewing (with varying diversity of opinions), and with any arbitrary video size and type. The users are found to be motivated by their sense of responsibility. This paper also highlights the importance of identifying spammers through such models.

Time-sensitive clustering evolving textual data streams

Clustering a stream of text documents is an emerging subject of interest since it is widely used in analysing the content in social media and e-journals. The aim is to find a certain structure for unlabelled data based on a similarity criterion. However, few works have focused on this field and fall in this perspective, that's why a new document clustering approach adapted to a stream of text data and test it on news articles data sets is proposed. A distributed representation of words is used, and a bottom-up approach is used to represent documents as vectors on a unit hyper-sphere. The proposed approach gains its roots from the SPherical k-means (SPKM) algorithm and its underlying mixture of von-Mises Fisher (vMF) distributions. The proposed approach yields comparable results to baseline batch algorithm for stable data streams and superior results for rapidly evolving data streams.

Life Writing When the World is Burning: The Year in Australia

Life Writing When the World is BurningThe Year in Australia Kylie Cardell (bio) It is no surprise that in Australia this year a great deal of life writing has continued to emerge in conjunction with pressing social and political issues. The ongoing national crises of refugee and asylum seeker policy, gendered abuse, and racial discrimination continue to surface in both political and literary arenas, while unprecedented bushfires have decimated the country, bringing climate change back onto the public agenda with new fury. The right of individuals to live with dignity, in safety, and free from fear—and the ongoing challenges to these rights suffered in public and domestic domains—is a connecting thread across the year's life writing and a theme the genre is uniquely equipped to amplify. Writing Asylum In Australia, one of the most volatile issues for representation has been, for a number of years now, in relation to the experiences and lives of refugees who arrive (or attempt to arrive) in Australia by boat. In 2013, the Kurdish journalist and artist Behrouz Boochani was one of sixty asylum seekers intercepted by Australian authorities on a boat that had left from Indonesia. Between 2013 and 2017, Boochani was forcibly detained at the Australian-run Manus Island immigration detention center in Papua New Guinea (PNG), a facility that closed suddenly (and violently) in 2017 causing displacement and statelessness for hundreds of "unprocessed" detainees. In detainment and exile, Boochani has produced a remarkable body of testimony, including a documentary film and a series of diary-essays for mainstream news media. Boochani's memoir No Friend but the Mountains (2018), written from the Manus camp on a cell phone and delivered over WhatsApp to collaborators in Australia, was awarded in 2019 the Victorian Prize for Literature, Australia's richest literary prize (Wahlquist). Boochani was not permitted to attend the ceremony, a gala event held in Melbourne. When testimony turns to memoir, it takes the form of a prestigious literary product. No longer a [End Page 1] digital stream of text and images but a hardcover, prize-winning book, the account claims coveted territory in the Australian literary sphere—but the author is yet to set foot on the Australian mainland. In No Friend, Boochani testifies in lyrical, shocking detail to the lack of safety and the ongoingness of daily struggle that characterize life in the detention facility—this is "prison literature" and strategic activism, poetry and diary, memoir and novel. His story does work his body cannot; it circulates in the Australian public sphere, raising awareness, encouraging activism, and speaking on behalf of refugees. What the memoir might achieve in terms of legal rights for asylum seekers in Australia remains to be seen, but it is hopeful that No Friend has won such a significant prize this year. Writing Domestic Abuse In her memoir Fake: A Startling True Story of Love in a World of Liars, Cheats, Narcissists, Fantasists and Phonies (2019), Stephanie Wood uses life writing to work through an individual experience to achieve both emotional catharsis and visibility for widespread issues of gendered emotional abuse. Stephanie is a successful journalist writing for some of the country's top mainstream media publications when she meets a man named Joe through an online dating service. Newly forty, childless, and a long-term romantic who has nurtured a dream of "the one," Stephanie is also at an emotional nadir. The tension in Wood's story is built on disbelief: the reader can't believe Stephanie is falling for the increasingly implausible stories that Joe is crafting—he explains away a nonexistent house, denies a very much existing wife and children—and Stephanie doesn't believe in herself. Her inner monologue is relentless; she is too needy, too anxious, too untrusting, too possessive. By the time Stephanie manages to end the relationship, she has spiraled into a deep depression. Her recovery involves rediscovering her journalistic power: she teams up with a female colleague, she tracks land records and bankruptcy deeds, and she interviews the "other woman," an ex-partner who Joe was dating at the same time. She consults psychoanalysts about whether or not Joe's behavior might qualify as psychopathic...

ANaN — ANalyse And Navigate: Debugging Compute Clusters with Techniques from Functional Programming and Text Stream Processing

Monitoring is an indispensable tool for the operation of any large installation of grid or cluster computing, be it high energy physics or elsewhere. Usually, monitoring is configured to collect a small amount of data, just enough to enable detection of abnormal conditions. Once detected, the abnormal condition is handled by gathering all information from the affected components. This data is processed by querying it in a manner similar to a database. This contribution shows how the metaphor of a debugger (for software applications) can be transferred to a compute cluster. The concepts of variables, assertions and breakpoints that are used in debugging can be applied to monitoring by defining variables as the quantities recorded by monitoring and breakpoints as invariants formulated via these variables. It is found that embedding fragments of a data extracting and reporting tool such as the UNIX tool awk facilitates concise notations for commonly used variables since tools like awk are designed to process large event streams (in textual representations) with bounded memory. A functional notation similar to both the pipe notation used in the UNIX shell and the point-free style used in functional programming simplify the combination of variables that commonly occur when formulating breakpoints.

Generation of topic evolution graphs from short text streams

Topic evolution mining on short texts is an important research topic in natural language processing. Existing methods have been focused either on the topic evolution of normal documents or on the evolution of topics along a timeline. In this paper, we aim to generate topic evolutionary graphs from short texts, which not only capture the main topic timeline, but also reveal the correlations between related subtopics. Firstly, we propose an Encoder-only Transformer Language Model (ETLM) to quantify the relationship between words. Then we propose a novel topic model, referred as weighted Conditional random field regularized Correlated Topic Model (CCTM), which leverages semantic correlations to discover meaningful topics and topic correlations. Finally, topic evolutionary graphs are generated by an Online version of CCTM (OCCTM) to capture the evolutionary patterns of main topics and related subtopics. Experimental results on real-world datasets demonstrate our method outperforms baselines on quality of topics and presents motivated patterns for topic evolution mining.

Ring: Real-Time Emerging Anomaly Monitoring System Over Text Streams

Microblog platforms have been extremely popular in the big data era due to its real-time diffusion of information. It's important to know what anomalous events are trending on the social network and be able to monitor their evolution and find related anomalies. In this paper we demonstrate Ring , a r eal-t i me emerging a n omaly monitorin g system over microblog text streams. Ring integrates our efforts on both emerging anomaly monitoring research and system research. From the anomaly monitoring perspective, Ring proposes a graph analytic approach such that (1) Ring is able to detect emerging anomalies at an earlier stage compared to the existing methods, (2) Ring is among the first to discover emerging anomalies correlations in a streaming fashion, (3) Ring is able to monitor anomaly evolutions in real-time at different time scales from minutes to months. From the system research perspective, Ring (1) optimizes time-ranged keyword query performance of a full-text search engine to improve the efficiency of monitoring anomaly evolution, (2) improves the dynamic graph processing performance of Spark and implements our graph stream model on it, As a result, Ring is able to process big data to the entire Weibo or Twitter text stream with linear horizontal scalability. The system clearly presents its advantages over existing systems and methods from both the event monitoring perspective and the system perspective for the emerging event monitoring task.

Classification of Text Documents Based on a Probabilistic Topic Model

An approach to text document classification that utilizes a probabilistic topic model, which is characterized by the fact that its training document set contains objects of only one class, is proposed. This approach makes it possible to identify positive samples (samples resembling the target class) in collections and streams of text documents. This article considers models created for solving the problems of text document classification and trained on samples of a single class, describes their key features. The Positive Example Based Learning-TM classification model is presented and a software prototype that implements it as a basis for classification of text documents is developed. Despite having no information about negative document samples, the model demonstrates a high level of classification accuracy that exceeds the performance of alternative approaches. The superiority of the Positive Example Based Learning-TM model with respect to the classification accuracy criterion when using a small training set is experimentally proven.

Visual abstraction and exploration of large-scale geographical social media data

A great deal of text and geographical information is provided in the geo-tagged social media data, which offers unprecedented opportunities to get insights into the social behaviors across different local areas. With the increasing size of geo-tagged social media data, a large number of visual mapping elements overlap with each other, which makes it difficult to visually capture topics of interest as well as their spatial distribution. In this paper, we propose a visual abstraction framework for the exploration of large scale geo-tagged social media data. Probabilistic topic modeling is firstly utilized to summarize the semantics of texts and extract a set of topic features of interest. Then, a multi-objective sampling model is designed to generate a subset of original dataset, which will not only reduce the visual clutter of large-scale social media data visualization, but also retain the ordering of topic features of interest as well as the geographical distribution of original social media datasets. A rich set of visual designs such as word cloud, text stream and heat map are integrated into the visual abstraction framework, enabling users to evaluate the sampled results from different perspectives including semantic topics, temporal changes and spatial distribution. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in simplifying the geographical visualization of large scale geo-tagged social media data and exploring the social behaviors across different local areas.

LEXICAL PARADIGMATICS AND ITS VARIANTS

The article deals with the issues of lexical paradigms from the point of view of their application in various layers of vocabulary. The term “paradigm” has been analysed from different viewpoints. The works of Ferdinand de Saussure, L. Elmslev, A. S. Pardaev and other authors are considered. Special attention is paid to syntagmatic and associative (paradigmatic) relations between language units. Paradigmatic relations,unlike syntagmatic relations, are considered as non-linear and non-simultaneous in a stream of speech or text, they are represented as relations between elements of a language that are united in the mind or memory of a person by some association. Based on the fact that the main constituent elements of lexical paradigms are antonyms, homonyms, paronyms, synonyms, groups, semantic fields, the main emphasis is placed on the study of these lexical units. Synonymy has been studied in terms of the degree of similarity, identity, and proximity of meanings. Antonyms, unlike synonyms, are based on the opposition of meanings. Relationships of closeness and opposition of semantics are logically reduced to four types of oppositions: zero, privative, equipalent, and disjunctive. Each of them has its own type of semantic relations: relations of identity, inclusion, intersection, or exclusion. The article also discusses the question of the synonyms of the meaning of the terms "lexeme" and "word", "lexical meaning" and "lexico-semantic variant". Since the organization of vocabulary has close interaction with lexical paradigms, examples of the use of synonyms and antonyms in linguistic dictionaries are given. In particular, the option for bringing a synonymic series to protect are considered on the example of the “Dictionary of Synonyms of the Russian Language” by A.P. Evgeneva and the Antonymic pair CLOSE-FAR in the “Dictionary of Antonyms of the Russian Language” by M.R.Lvov.

The Voices of European Law: Legislators, Judges and Law Professors

EU legislators, CJEU judges and EU law scholars have produced streams of texts which determine both what EU law is and what it is perceived to be about. In this paper, we explore what these distinct “voices” tell us about the EU’s legal and policy priorities using a mega corpus compiling more than 200 000 legislative acts, 55 000 court rulings and opinions and 4000 articles from a leading EU law journal. Applying a text-mining technique known as probabilistic topic modelling, we find that economic integration remains the focus of EU law but that scholars tend to emphasise rights issues more while ignoring certain topics, such as farming regulations, almost entirely.

Collaborative, Dynamic and Diversified User Profiling

In this paper, we study the problem of dynamic user profiling in the context of streams of short texts. Previous work on user profiling works with long documents, do not consider collaborative information, and do not diversify the keywords for profiling users’ interests. In contrast, we address the problem by proposing a user profiling algorithm (UPA), which consists of two models: the proposed collaborative interest tracking topic model (CITM) and the proposed streaming keyword diversification model (SKDM). UPA first utilizes CITM to collaboratively track each user’s and his followees’ dynamic interest distributions in the context of streams of short texts, and then utilizes SKDM to obtain top-k relevant and diversified keywords to profile users’ interests at a specific point in time. Experiments were conducted on a Twitter dataset and we found that UPA outperforms state-of-the-art non-dynamic and dynamic user profiling algorithms.