Echoes of Persuasion: The Effect of Euphony in Persuasive Communication

While the effect of various lexical, syntactic, semantic and stylistic features have been addressed in persuasive language from a computational point of view, the persuasive effect of phonetics has received little attention. By modeling a notion of euphony and analyzing four datasets comprising persuasive and non-persuasive sentences in different domains (political speeches, movie quotes, slogans and tweets), we explore the impact of sounds on different forms of persuasiveness. We conduct a series of analyses and prediction experiments within and across datasets. Our results highlight the positive role of phonetic devices on persuasion

Exploring Text Virality in Social Networks

This paper aims to shed some light on the concept of virality - especially in social networks - and to provide new insights on its structure. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread, rather than to the influencers who spread it, (b) virality is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised and they only partially overlap. To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be independently predicted according to content features

The PBSDS: A dataset for the detection of pseudoprofound bullshit

“Bullshit" refers to communication that is designed to impress but is constructed without concern for truth [1]. Bullshit differs from lying in that the liar deliberately manipulates and subverts truth (usually with the intent to deceive), while the bullshitter is simply unconcerned with what is true and what is false. A liar needs to know the truth value of a proposition; the bullshitter simply does not care. Although bullshit comes in different forms, in this project, we focused specifically on what is referred to as “pseudoprofound bullshit," which is designed to convey some sort of potentially profound meaning but is actually semantically vacuous [2], e.g., “Hidden meaning transforms unparalleled abstract beauty." Table 1 reports further examples of pseudoprofound bullshit and nonpseudoprofound bullshit sentences from our dataset. The goal of this project is to construct a dataset of tweets that contain pseudoprofound bullshit in English (the PBSDS).1 Operating under the assumption that bullshit is similar to spam email, we hypothesize that it should be possible to detect pseudoprofound bullshit using relatively simple classification algorithms

Ecological Evaluation of Persuasive Messages Using Google AdWords

In recent years there has been a growing interest in crowdsourcing methodologies to be used in experimental research for NLP tasks. In particular, evaluation of systems and theories about persuasion is difficult to accommodate within existing frameworks. In this paper we present a new cheap and fast methodology that allows fast experiment building and evaluation with fully-automated analysis at a low cost. The central idea is exploiting existing commercial tools for advertising on the web, such as Google AdWords, to measure message impact in an ecological setting. The paper includes a description of the approach, tips for how to use AdWords for scientific research, and results of pilot experiments on the impact of affective text variations which confirm the effectiveness of the approach.Comment: To appear at ACL 2012. 9 pages, 2 figure

Aligning verb senses in two Italian lexical semantic resources

National audienceThis work describes the evaluations of three different approaches, Lexical Match, Sense Similarity based on Personalized Page Rank, and Semantic Match based on Shallow Frame Structures, for word sense alignment of verbs between two Italian lexical-semantic resources, MultiWordNet and the Senso Comune Lexicon. The results obtained are quite satisfying with a final F1 score of 0.47 when merging together Lexical Match and Sense Similarity

Enriching the "Senso Comune" Platform with Automatically Acquired Data

International audienceThis paper reports on research activities on automatic methods for the enrichment of the Senso Comune platform. At this stage of development, we will report on two tasks, namely word sense alignment with MultiWordNet and automatic acquisition of Verb Shallow Frames from sense annotated data in the MultiSemCor corpus. The results obtained are satisfying. We achieved a final F-measure of 0.64 for noun sense alignment and a F-measure of 0.47 for verb sense alignment, and an accuracy of 68% on the acquisition of VerbShallow Frames

Aligning an Italian WordNet with a lexicographic dictionary: Coping with limited data

International audienceThis work describes the evaluations of two approaches, Lexical Matching and Sense Similarity, for word sense alignment between MultiWordNet and a lexicographic dictionary, Senso Comune De Mauro, when having few sense descriptions (MultiWordNet) and no structure over senses (Senso Comune De Mauro). The results obtained from the merging of the two approaches are satisfying, with F1 values of 0.47 for verbs and 0.64 for nouns

a computational analysis of the language of drug addiction

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word etymology as native language interference

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Exploring high-level features for detecting cyberpedophilia

[EN] In this paper, we suggest a list of high-level features and study their applicability in detection of cyberpedophiles. We used a corpus of chats downloaded from http://www.perverted-justice.com and two negative datasets of different nature: cybersex logs available online, and the NPS chat corpus. The classification results show that the NPS data and the pedophiles’ conversations can be accurately discriminated from each other with character n-grams, while in the more complicated case of cybersex logs there is need for high-level features to reach good accuracy levels. In this latter setting our results show that features that model behaviour and emotion significantly outperform the low-level ones, and achieve a 97% accuracy.The work of Dasha Bogdanova was partially carried out during the internship at the Universitat Politecnica de Valencia (scholarship of the University of St. Petersburg). Her research was partially supported by Google Research Award. The collaboration with Thamar Solorio was possible thanks to her one-month research visit at the Universitat Politecnica de Valencia (program PAID-PAID-02-11 award n. 1932). The research work of Paolo Rosso was done in the framework of the European Commission WIQ-EI Web Information Quality Evaluation Initiative (IRSES Grant No. 269180) project within the FP 7 Marie Curie People, the DIANA-APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-0O2-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Bogdanova, D.; Rosso, P.; Solorio, T. (2014). Exploring high-level features for detecting cyberpedophilia. Computer Speech and Language. 28(1):108-120. https://doi.org/10.1016/j.csl.2013.04.007S10812028