Internet Defamation as Profit Center: The Monetization of Online Harassment

Efforts to decrease the sexist aspects of online fora have been largely ineffective, and in some instances seemingly counterproductive, in the sense that they have provoked even greater amounts of abuse and harassment with a gendered aspect. And so, in the wake of a series of high profile episodes of cyber sexual harassment, and a grotesque abundance of low profile ones, a new business model was launched. Promising to clean up and monitor online information to defuse the visible impact of coordinated harassment campaigns, a number of entities began to market themselves as knights in cyber shining armor, ready to defend otherwise defenseless people whose reputations have been sullied on the Internet Of course these companies charge a fee and place particular emphasis on women who they recognize as potential clients. This article raises three concerns about these businesses. First, these companies have economic incentives to foster conditions online that perpetuate acts of online harassment, as the more harassment there is online, the greater the number of potential clients. These companies are also incentivized to create fora with hostile climates and to stir up trouble themselves. Second, these companies have economic incentives to oppose legal reforms that might enable online defamation and harassment victims to seek recourse from law enforcement agencies or through the courts. And finally, though they cloak themselves in the mantel of protectors of the innocent, their real agenda is to sell their services to wealthy corporations and individuals for far more nefarious purposes: to help bad actors hide negative information about themselves. This practice creates information asymmetries that can harm anyone who detrimentally relies on what they incorrectly assume to be the best available information and can lead to increases in the sorts of financial losses and personal vulnerability that access to unmanipulated Internet search results might otherwise reduce

Positive and Negative Sentiment Words in a Blog Corpus Written in Hebrew

AbstractIn this research, given a corpus containing blog posts written in Hebrew and two seed sentiment lists, we analyze the positive and negative sentences included in the corpus, and special groups of words that are associated with the positive and negative seed words. We discovered many new negative words (around half of the top 50 words) but only one positive word. Among the top words that are associated with the positive seed words, we discovered various first-person and third-person pronouns. Intensifiers were found for both the positive and negative seed words. Most of the corpus’ sentences are neutral. For the rest, the rate of positive sentences is above 80%. The sentiment scores of the top words that are associated with the positive words are significantly higher than those of the top words that are associated with the negative words.Our conclusions are as follows. Positive sentences more “refer to” the authors themselves (first-person pronouns and related words) and are also more general, e.g., more related to other people (third-person pronouns), while negative sentences are much more concentrated on negative things and therefore contain many new negative words. Israeli bloggers tend to use intensifiers in order to emphasize or even exaggerate their sentiment opinions (both positive and negative). These bloggers not only write much more positive sentences than negative sentences, but also write much longer positive sentences than negative sentences

Discovering a tourism destination with social media data: BERT-based sentiment analysis

Purpose – The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users. Design/methodology/approach – The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models. Findings – The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated. Research limitations/implications – The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place. Practical implications – The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions. Originality/value – This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion PID2019-106758GB-C31European Commissio

A syntactic approach for opinion mining on Spanish reviews

This accepted version of the article has been published in a revised form in Natural Language Engineering, 21(1), 139-163. https://doi.org/10.1017/S1351324913000181 . This version is published under a Creative Commons CC-BY-NC-ND licence. No commercial re-distribution or re-use allowed. Derivative works cannot be distributed. © Cambridge University Press 2013 .[Abstract]: We describe an opinion mining system which classifies the polarity of Spanish texts. We propose an NLP approach that undertakes pre-processing, tokenisation and POS tagging of texts to then obtain the syntactic structure of sentences by means of a dependency parser. This structure is then used to address three of the most significant linguistic constructions for the purpose in question: intensification, subordinate adversative clauses and negation. We also propose a semi-automatic domain adaptation method to improve the accuracy of our system in specific application domains, by enriching semantic dictionaries using machine learning methods in order to adapt the semantic orientation of their words to a particular field. Experimental results are promising in both general and specific domains.Research reported in this paper has been partially funded by Ministerio de Economía y Competitividad and FEDER (grant TIN2010-18552-C03-02) and by Xunta de Galicia (grants CN2012/008, CN2012/319). We thank Maite Taboada for giving us access to SODictionariesV1.11Spa.Xunta de Galicia; CN2012/008Xunta de Galicia; CN2012/31

Opinion mining: Reviewed from word to document level

International audienceOpinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks

Deductions from a Sub-Saharan African bank’s tweets: A sentiment analysis approach

The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper. © 2020, © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.Tomas Bata University in Zlin [IGA/CebiaTech/2020/001

Diverse virtual social networks: Implications for remote software testing teams

This paper evaluates offshore outsourcing in the IT testing industry and determines what conditions determine its success. There is particular focus on the influence of diversity in teams on group relationships. Two studies are described: the first, investigated the perceptions of professional software testers on the critical factors of offshore outsourcing; and the second study looked at the ability for diverse teams to form close working relationships through virtual networks. We find that overt diversity factors inhibit interaction across nationality boundaries. The limitations of virtual networks for fostering personal communications is apparent in preventing group members from overcoming the initial aversion to mix with out-group members, which could be achieved with closer and more personal communications between members with different diversity factors in normal face to face communications. Where software testing teams are outsourced globaly, and must rely on virtual communications, there seems potential for significant difficulties in developing close working relationships, which on the one hand, can be negative for group cohesion, but one the other hand, can be positive for encouraging imparitality