Abstract
The burst of social networks and the possibility of being continuously connected has provided a fast way for information diffusion. More specifically, real-time posting allowed news and events to be reported quicker through social networks than traditional news media. However, the massive data that is daily available makes newsworthy information a needle in a haystack. Therefore, our goal is to build models that can detect journalistic relevance automatically in social networks. In order to do it, it is essential to establish a ground truth with a large number of entries that can provide a suitable basis for the learning algorithms due to the difficulty inherent to the ambiguity and wide scope associated with the concept of relevance. In this paper, we propose and compare two different methodologies to annotate posts regarding their relevance: automatic and human annotation. Preliminary results show that supervised models trained with the automatic annotation methodology tend to perform better than using human annotation in a test dataset labeled by experts.

Abstract
Sentiment lexicons are an essential component on most state-of-the-art sentiment analysis methods. However, the terms included are usually restricted to verbs and adjectives because they (1) usually have similar meanings among different domains and (2) are the main indicators of subjectivity in the text. This can lead to a problem in the classification of short informal texts since sometimes the absence of these types of parts of speech does not mean an absence of sentiment. Therefore, our hypothesis states that knowledge of terms regarding certain events and respective sentiment (public opinion) can improve the task of sentiment analysis. Consequently, to complement traditional sentiment dictionaries, we present a system for lexicon expansion that extracts the most relevant terms from news and assesses their positive or negative score through Twitter. Preliminary results on a labelled dataset show that our complementary lexicons increase the performance of three state-of-the-art sentiment systems, therefore proving the effectiveness of our approach.

Abstract
Misinformation propagation on social media has been significantly growing, reaching a major exposition in the 2016 United States Presidential Election. Since then, the scientific community and major tech companies have been working on the problem to avoid the propagation of misinformation. For this matter, research has been focused on three major sub-fields: the identification of fake news through the analysis of unreliable posts, the propagation patterns of posts in social media, and the detection of bots and spammers. However, few works have tried to identify the characteristics of a post that shares unreliable content and the associated behaviour of its account. This work presents four main contributions for this problem. First, we provide a methodology to build a large knowledge database with tweets who disseminate misinformation links. Then, we answer research questions on the data with the goal of bridging these problems to similar problem explored in the literature. Next, we focus on accounts which are constantly propagating misinformation links. Finally, based on the analysis conducted, we develop a model to detect social media accounts that spread unreliable content. Using Decision Trees, we achieved 96% in the F1-score metric, which provides reliability on our approach. Copyright 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Abstract
Nowadays, false news can be created and disseminated easily through the many social media platforms, resulting in a widespread real-world impact. Modeling and characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient algorithms and tools for their early detection. A recent surge of researching in this area has aimed to address the key issues using methods based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, together with newly created data sets and web services to identify deceiving content. Majority of the research has been targeting fake reviews, biased messages, and against-facts information (false news and hoaxes). In this work, we present a survey on the state of the art concerning types of fake news and the solutions that are being proposed. We focus our survey on content analysis, network propagation, fact-checking and fake news analysis and emerging detection systems. We also discuss the rationale behind successfully deceiving readers. Finally, we highlight important challenges that these solutions bring. Copyright

Abstract
As organizations are entering social media, determining their current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the evaluation of social media strategies' and eventual readjustments, and a subsequent efficiency measurement. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. To address these challenges, we propose an automatic procedure to assess the posting behavior and strategy identification for each higher educational institution. We used a sample of the 10-top worldwide ranked educational institutions in this study and collected the posts from their official Facebook pages during an entire school year. Our study was conducted on the frequency and intensity of publications by universities, which included an analysis of the number of responses to 'posts' over time in the form of 'shares'. Finally, the content of the posts was analyzed according to the topics covered in the messages. This process allowed us to identify the editorial areas that each university uses the most and in which are more active. © 2018 The Authors. Published by Elsevier Ltd..

Abstract
Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics.