“Got You!”: Automatic Vandalism Detection in Wikipedia with Web-based Shallow Syntactic-Semantic Modeling

Discriminating vandalism edits from non-vandalism edits in Wikipedia is a challenging task, as ill-intentioned edits can include a variety of content and be expressed in many different forms and styles. Previous studies are limited to rule-based methods and learning based on lexical features, lacking in linguistic analysis. In this paper, we propose a novel Web-based shallow syntactic-semantic modeling method, which utilizes Web search results as resource and trains topic-specific n-tag and syntactic n-gram language models to detect vandalism. By combining basic task-specific and lexical features, we have achieved high F-measures using logistic boosting and logistic model trees classifiers, surpassing the results reported by major Wikipedia vandalism detection systems

“How Short is a Piece of String?”: An Investigation into the Impact of Text Length on Short-Text Classification Accuracy

The recent increase in the widespread use of short messages, for example micro-blogs or SMS communications, has created an opportunity to harvest a vast amount of information through machine-based classification. However, traditional classification methods have failed to produce accuracies comparable to those obtained from similar classification of longer texts. Several approaches have been employed to extend traditional methods to overcome this problem, including the enhancement of the original texts through the construction of associations with external data enrichment sources, ranging from thesauri and semantic nets such as Wordnet, to pre-built online taxonomies such as Wikipedia. Other avenues of investigation have used more formal extensions such as Latent Semantic Analysis (LSA) to extend or replace the more basic, traditional, methods better suited to classification of longer texts. This work examines the changes in classification accuracy of a small selection of classification methods using a variety of enhancement methods, as target text length decreases. The experimental data used is a corpus of micro-blog (twitter) posts obtained from the ‘Sentiment140’1 sentiment classification and analysis project run by Stanford University and described by Go, Bhayani and Huang (2009), which has been split into sub-corpora differentiated by text length

Human-competitive automatic topic indexing

Topic indexing is the task of identifying the main topics covered by a document. These are useful for many purposes: as subject headings in libraries, as keywords in academic publications and as tags on the web. Knowing a document's topics helps people judge its relevance quickly. However, assigning topics manually is labor intensive. This thesis shows how to generate them automatically in a way that competes with human performance. Three kinds of indexing are investigated: term assignment, a task commonly performed by librarians, who select topics from a controlled vocabulary; tagging, a popular activity of web users, who choose topics freely; and a new method of keyphrase extraction, where topics are equated to Wikipedia article names. A general two-stage algorithm is introduced that first selects candidate topics and then ranks them by significance based on their properties. These properties draw on statistical, semantic, domain-specific and encyclopedic knowledge. They are combined using a machine learning algorithm that models human indexing behavior from examples. This approach is evaluated by comparing automatically generated topics to those assigned by professional indexers, and by amateurs. We claim that the algorithm is human-competitive because it chooses topics that are as consistent with those assigned by humans as their topics are with each other. The approach is generalizable, requires little training data and applies across different domains and languages

Controversy trend detection in social media

In this research, we focus on the early prediction of whether topics are likely to generate significant controversy (in the form of social media such as comments, blogs, etc.). Controversy trend detection is important to companies, governments, national security agencies, and marketing groups because it can be used to identify which issues the public is having problems with and develop strategies to remedy them. For example, companies can monitor their press release to find out how the public is reacting and to decide if any additional public relations action is required, social media moderators can moderate discussions if the discussions start becoming abusive and getting out of control, and governmental agencies can monitor their public policies and make adjustments to the policies to address any public concerns. An algorithm was developed to predict controversy trends by taking into account sentiment expressed in comments, burstiness of comments, and controversy score. To train and test the algorithm, an annotated corpus was developed consisting of 728 news articles and over 500,000 comments on these articles made by viewers from CNN.com. This study achieved an average F-score of 71.3% across all time spans in detection of controversial versus non-controversial topics. The results suggest that it is possible for early prediction of controversy trends leveraging social media

Detección de discurso de odio online utilizando Machine Learning

Trabajo de Fin de Grado en Ingeniería informática, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022. Enlace al repositorio público del proyecto: https://github.com/NILGroup/TFG-2122HateSpeechDetectionHate speech directed towards marginalized people is a very common problem online, especially in social media such as Twitter or Reddit. Automatically detecting hate speech in such spaces can help mend the Internet and transform it into a safer environment for everybody. Hate speech detection fits into text classification, a series of tasks where text is organized into categories. This project2 proposes using Machine Learning algorithms to detect hate speech in online text in four languages: English, Spanish, Italian and Portuguese. The data to train the models was obtained from online, publicly available datasets. Three different algorithms with varying parameters have been used in order to compare their performance. The experiments show that the best results reach an 82.51% accuracy and around an 83% F1-score, for Italian text. Each language has different results depending on distinct factors.El discurso de odio dirigido a personas marginadas es un problema muy común en línea, especialmente en redes sociales como Twitter o Reddit. La detección automática del discurso de odio en dichos espacios puede ayudar a reparar Internet y a transformarlo en un entorno más seguro para todos. La detección del discurso de odio encaja en la clasificación de texto, donde se organiza en categorías. Este proyecto1 propone el uso de algoritmos de Machine Learning para localizar discurso de odio en textos online en cuatro idiomas: inglés, español, italiano y portugués. Los datos para entrenar los modelos se obtuvieron de datasets disponibles públicamente en línea. Se han utilizado tres algoritmos diferentes con distintos parámetros para comparar su rendimiento. Los experimentos muestran que los mejores resultados alcanzan una precisión del 82,51 % y un valor F1 de alrededor del 83 % en italiano. Los resultados para cada idioma varían dependiendo de distintos factores.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Pattern based fact extraction from Estonian free-texts

Vabatekstide töötlus on üks keerulisemaid probleeme arvutiteaduses. Tekstide täpne analüüs on tihti mitmestimõistetavuse tõttu arvutite jaoks keeruline või võimatu. Sellegipoolest on võimalik teatud fakte eraldada. Käesolevas töös uurime mustripõhiseid meetodeid faktide tuletamiseks eesti keelsetest tekstidest. Rakendame oma metoodikat reaalsetel tekstidel ning analüüsime tulemusi. Kirjeldame lühidalt aktiivõppe metoodikat, mis võimaldab suuri korpuseid kiiremini märgendada. Lisaks oleme implementeerinud prototüüplahenduse korpuste märgendamiseks ning mustripõhise faktituletuse läbiviimiseks.Natural language processing is one of the most difficult problems, since words and language constructions have often ambiguous meaning that cannot be resolved without extensive cultural background. However, some facts are easier to deduce than the others. In this work, we consider unary, binary and ternary relations between the words that can be deduced form a single sentence. The relations represented by sets of patterns are combined with basic machine learning methods, that are used to train and deploy patterns for fact extraction. We also describe the process of active learning, which helps to speed up annotating relations in large corpora. Other contributions include a prototype implementation with plain-text preprocessor, corpus annotator, pattern miner and fact extractor. Additionally, we provide empirical study about the efficiency of the prototype implementation with several relations and corpora

Identifying personality and topics of social media

Title from PDF of title page viewed January 27, 2020Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 37-39)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019Twitter and Facebook are the renowned social networking platforms where users post, share, interact and express to the world, their interests, personality, and behavioral information. User-created content on social media can be a source of truth, which is suitable to be consumed for the personality identification of social media users. Personality assessment using the Big 5 personality factor model benefits organizations in identifying potential professionals, future leaders, best-fit candidates for the role, and build effective teams. Also, the Big 5 personality factors help to understand depression symptoms among aged people in primary care. We had hypothesized that understanding the user personality of the social network would have significant benefits for topic modeling of different areas like news, towards understanding community interests, and topics. In this thesis, we will present a multi-label personality classification of the social media data and topic feature classification model based on the Big 5 model. We have built the Big 5 personality classification model using a Twitter dataset that has defined openness, conscientiousness, extraversion, agreeableness, and neuroticism. In this thesis, we (1) conduct personality detection using the Big 5 model, (2) extract the topics from Facebook and Twitter data based on each personality, (3) analyze the top essential topics, and (4) find the relation between topics and personalities. The personality would be useful to identify what kind of personality, which topics usually talk about in social media. Multi-label classification is done using Multinomial Naïve Bayes, Logistic Regression, Linear SVC. Topic Modeling is done based on LDA and KATE. Experimental results with Twitter and Facebook data demonstrate that the proposed model has achieved promising results.Introduction -- Background and related work -- Proposed framework -- Results and evaluations -- Conclusion and future wor