twazn me!!! ;(’ Automatic Authorship Analysis of Micro-Blogging Messages (original) (raw)
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Whose Tweet? Authorship analysis of micro-blogs and other short-form messages
Approaches to authorship attribution have traditionally been constrained by the size of the message to which they can be successfully applied, making them unsuitable for analysing shorter messages such as SMS Text Messages, micro-blogs (e.g. Twitter) or Instant Messaging. Having many potential authors of a number of texts (as in, for example, an online context) has also proved problematic for traditional descriptive methods, which have tended to be successfully applied in cases where there is a small and closed set of possible authors. This paper reports the findings of a project which aimed to develop and automate techniques from forensic linguistics that have been successfully applied to the analysis of short message content in criminal cases. Using data drawn from UK-focused online groups within Twitter, the research extends the applicability of Grant's (2007; 2010) stylistic and statistical techniques for the analysis of authorship of short texts into the online environment. Initial identification of distinctive textual features commonly found within short messages allows for the development of a taxonomy which can then be used when calculating the 'distance' between messages containing instances of these feature types. The end result is an automated process with a high level of success in assigning tweets to the correct author. The research has the potential to extend the scope of reliable and valid authorship analysis into hitherto unexplored contexts. Given the relative anonymity of the internet and the availability of cloaking technology, linguistic research of this nature represents a crucial contribution to the investigative toolkit.
Authorship Attribution for Online Social Media
2018
This chapter gives a comprehensive knowledge of various machine learning classifiers to achieve authorship attribution (AA) on short texts, specifically tweets. The need for authorship identification is due to the increasing crime on the internet, which breach cyber ethics by raising the level of anonymity. AA of online messages has witnessed interest from many research communities. Many methods such as statistical and computational have been proposed by linguistics and researchers to identify an author from their writing style. Various ways of extracting and selecting features on the basis of dataset have been reviewed. The authors focused on n-grams features as they proved to be very effective in identifying the true author from a given list of known authors. The study has demonstrated that AA is achievable on the basis of selection criteria of features and methods in small texts and also proved the accuracy of analysis changes according to combination of features. The authors fou...
More Blogging Features for Authorship Identification
In this paper we present a novel improvement in the field of authorship identification in personal blogs. The improvement in authorship identification, in our work, is by utilizing a hybrid collection of linguistic features that best capture the style of users in diaries blogs. The features sets contain LIWC with its psychology background, a collection of syntactic features & part-of-speech (POS), and the misspelling errors features. Furthermore, we analyze the contribution of each feature set on the final result and compare the outcome of using different combination from the selected feature sets. Our new categorization of misspelling words which are mapped into numerical features, are noticeably enhancing the classification results. The paper also confirms the best ranges of several parameters that affect the final result of authorship identification such as the author numbers, words number in each post, and the number of documents/posts for each author/user. The results and evaluation show that the utilized features are compact, while their performance is highly comparable with other much larger feature sets.
Authorship verification for short messages using stylometry
2013 International Conference on Computer, Information and Telecommunication Systems (CITS), 2013
Authorship verification can be checked using stylometric techniques through the analysis of linguistic styles and writing characteristics of the authors. Stylometry is a behavioral feature that a person exhibits during writing and can be extracted and used potentially to check the identity of the author of online documents. Although stylometric techniques can achieve high accuracy rates for long documents, it is still challenging to identify an author for short documents, in particular when dealing with large authors populations. These hurdles must be addressed for stylometry to be usable in checking authorship of online messages such as emails, text messages, or twitter feeds. In this paper, we pose some steps toward achieving that goal by proposing a supervised learning technique combined with n-gram analysis for authorship verification in short texts. Experimental evaluation based on the Enron email dataset involving 87 authors yields very promising results consisting of an Equal Error Rate (EER) of 14.35% for message blocks of 500 characters.
Journal of The American Society for Information Science and Technology, 2006
With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.
arXiv Computer Science, 2020
In recent years, messages and text posted on the Internet are used in criminal investigations. Unfortunately, the authorship of many of them remains unknown. In some channels, the problem of establishing authorship may be even harder, since the length of digital texts is limited to a certain number of characters. In this work, we aim at identifying authors of tweet messages, which are limited to 280 characters. We evaluate popular features employed traditionally in authorship attribution which capture properties of the writing style at different levels. We use for our experiments a self-captured database of 40 users, with 120 to 200 tweets per user. Results using this small set are promising, with the different features providing a classification accuracy between 92% and 98.5%. These results are competitive in comparison to existing studies which employ short texts such as tweets or SMS.
Conversationally-inspired Stylometric Features for Authorship Attribution in Instant Messaging
Authorship attribution (AA) aims at recognizing automati-cally the author of a given text sample. Traditionally applied to literary texts, AA faces now the new challenge of recog-nizing the identity of people involved in chat conversations. These share many aspects with spoken conversations, but AA approaches did not take it into account so far. Hence, this paper tries to fill the gap and proposes two novelties that improve the effectiveness of traditional AA approaches for this type of data: the first is to adopt features inspired by Conversation Analysis (in particular for turn-taking), the second is to extract the features from individual turns rather than from entire conversations. The experiments have been performed over a corpus of dyadic chat conversations (77 in-dividuals in total). The performance in identifying the per-sons involved in each exchange, measured in terms of area under the Cumulative Match Characteristic curve, is 89.5%.
Forensic Authorship Analysis of Microblogging Texts Using N-Grams and Stylometric Features
2020 8th International Workshop on Biometrics and Forensics (IWBF), 2020
In recent years, messages and text posted on the Internet are used in criminal investigations. Unfortunately, the authorship of many of them remains unknown. In some channels, the problem of establishing authorship may be even harder, since the length of digital texts is limited to a certain number of characters. In this work, we aim at identifying authors of tweet messages, which are limited to 280 characters. We evaluate popular features employed traditionally in authorship attribution which capture properties of the writing style at different levels. We use for our experiments a self-captured database of 40 users, with 120 to 200 tweets per user. Results using this small set are promising, with the different features providing a classification accuracy between 92% and 98.5%. These results are competitive in comparison to existing studies which employ short texts such as tweets or SMS.
Style mining of electronic messages for multiple authorship discrimination
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03, 2003
This paper considers the use of computational stylistics for performing authorship attribution of electronic messages, addressing categorization problems with as many as 20 different classes (authors). Effective stylistic characterization of text is potentially useful for a variety of tasks, as language style contains cues regarding the authorship, purpose, and mood of the text, all of which would be useful adjuncts to information retrieval or knowledge-management tasks. We focus here on the problem of determining the author of an anonymous message, based only on the message text. Several multiclass variants of the Winnow algorithm were applied to a vector representation of the message texts to learn models for discriminating different authors. We present results comparing the classification accuracy of the different approaches. The results show that stylistic models can be learned to determine an author's identity with promising accuracy. This work thus forms a baseline for future research in author attribution of electronic messages.
The aim of this paper is to explore the usefulness of using features from different linguistic levels to email authorship identification. Using various email datasets provided by PAN'11 lab we tested several feature groups in both authorship attribution and authorship verification subtasks. The selected feature groups combined with Regularized Logistic Regression and One-Class SVM machine learning methods performed well above average in authorship attribution subtasks and below average in authorship verification subtasks.