Language and the Internet

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Boosting: Foundations and Algorithms.

This overview presents the Author Profiling shared task at PAN 2019. The focus of this year’s task is to determine whether the author of a Twitter feed is a bot or a human. Furthermore, in case of human, to profile the gender of the author. Two have been the main aims: i) to show the feasibility of automatically identifying bots in Twitter; and ii) to show the difficulty of identifying them when they do not limit themselves to just retweet domain-specific news. For this purpose a corpus with Twitter data has been provided, covering the languages English, and Spanish. Altogether, the approaches of 56 participants are evaluated.

/pdf/overview-of-the-7th-author-profiling-task-at-pan-2019-bots-3un9fiv8p3.pdf

Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling in Twitter.

First of all we thank the participants: 66 this year, record in terms of participants at PAN Lab since 2009! We have to thank also Martin Potthast, Matti
Wiegmann, and Nikolay Kolyada to help with the 66 Virtual Machines in the
TIRA platform. We thank Symanto for sponsoring the ex aequo award for the two best performing systems at the author profiling shared task of this year. The
work of Paolo Rosso was partially funded by the Spanish MICINN under the
research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31).
The work of Anastasia Giachanou is supported by the SNSF Early Postdoc
Mobility grant under the project Early Fake News Detection on Social Media,
Switzerland (P2TIP2 181441).

Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter

Feature selection refers to the problem of finding the optimal subset of features by removing irrelevant and redundant features to improve classification accuracy. The determination of the most effective distance measures to evaluate the relevance and redundancy of features has not been investigated precisely to date. Moreover, the relation between relevancy and redundancy is still uncertain. This paper presents a novel relevancy-redundancy measurement based on distance applying the idea of the mRMR criteria to an unsupervised method. In addition, a supervised method is proposed, in which the features are ranked in terms of the distance between each pair of samples in different classes of the feature vector. Then an ensemble of the proposed supervised and unsupervised methods is applied to choose the most relevant features subset. This study investigates and compares the effects of 24 distance measures selected from five major families of distance functions on the performance of the proposed feature selection methods. The highest-ranked features are selected using an empirically achieved threshold. To evaluate the selected features, three classifiers, i.e., Decision Tree, Support Vector Machine and Naive Bayes were applied to biomedical datasets representing binary problems from the UCI data repository. The experimental results demonstrate the superiority of the proposed methods over the state-of-the-art and also classical feature selection ones in terms of improving stability, classification accuracy, Recall (Sensitivity), Precision, F-measure, and Specificity. 

Ensemble feature selection using distance-based supervised and unsupervised methods in binary classification

To determine the author of a text's gender, various feature types have been suggested (e.g., function words, n‐gram of letters, etc.) leading to a huge number of stylistic markers. To determine the target category, different machine learning models have been suggested (e.g., logistic regression, decision tree, k nearest‐neighbors, support vector machine, naïve Bayes, neural networks, and random forest). In this study, our first objective is to know whether or not the same model always proposes the best effectiveness when considering similar corpora under the same conditions. Thus, based on 7 CLEF‐PAN collections, this study analyzes the effectiveness of 10 different classifiers. Our second aim is to propose a 2‐stage feature selection to reduce the feature size to a few hundred terms without any significant change in the performance level compared to approaches using all the attributes (increase of around 5% after applying the proposed feature selection). Based on our experiments, neural network or random forest tend, on average, to produce the highest effectiveness. Moreover, empirical evidence indicates that reducing the feature set size to around 300 without penalizing the effectiveness is possible. Finally, based on such reduced feature sizes, an analysis reveals some of the specific terms that clearly discriminate between the 2 genders.

Gender identification on Twitter

When participating in the “bots and gender” subtask (both in English and Spanish), our aim is to automatically detect different text sources (sequence of tweets sent by a bot or a human). When a text is identified as being sent by humans, the system must determine the author’s gender (author profiling). To solve these questions, we focus on a simple classifier (k-NN, k = 5) usually able to produce a correct answer but not in an efficient way. Thus, we apply a feature selection procedure to reduce the number of terms (around 200 to 500). We also propose to apply a Zeta model to reduce the number of decisions taken by the kNN classifier. In this case, we focus on terms used in one category and ignored or used rarely by the second. In addition, the Type-Token Ratio of the lexical density (LD) presents some merit to discriminate between tweets sent by a bot (TTR < 0.2, LD ≥ 0.8) or humans (TTR ≥ 0.2, LD < 0.8).

/pdf/unine-at-pan-clef-2019-bots-and-gender-task-436vecque5.pdf

UniNE at PAN-CLEF 2019: Bots and Gender Task.

In our participation of the “Profiling Fake News Spreaders on Twitter” task (both in English and Spanish), our main objective is to be able to detect Twitter user accounts used to spread disinformation, fake news, as well as conspiracy theories. To automatically solve these questions based only on the tweets' contents, we suggest to reduce the number of features (isolated words) to a few hundred. This suggested approach is based on a two-stage method ignoring infrequent terms and ranking the others according to their occurrence differences between the two categories. Finally, a classifier is implemented combining decision tree, random forest, and boosting. Our first evaluation experiments indicate an overall accuracy around 70%.

/pdf/unine-at-pan-clef-2020-profiling-fake-news-spreaders-on-5cl36cofsk.pdf

UniNE at PAN-CLEF 2020: Profiling Fake News Spreaders on Twitter.

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Gender identification on Twitter

UniNE at PAN-CLEF 2019: Bots and Gender Task.

UniNE at PAN-CLEF 2020: Profiling Fake News Spreaders on Twitter.