Sentiment dynamics in social media news channels

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012

The Web is a huge virtual space where to express and share individual opinions, influencing any aspect of life, with implications for marketing and communication alike. Social Media are influencing consumers' preferences by shaping their attitudes and behaviors. Monitoring the Social Media activities is a good way to measure customers' loyalty, keeping a track on their sentiment towards brands or products. Social Media are the next logical marketing arena. Currently, Facebook dominates the digital marketing space, followed closely by Twitter. This paper describes a Sentiment Analysis study performed on over than 1000 Facebook posts about newscasts, comparing the sentiment for Rai -the Italian public broadcasting service -towards the emerging and more dynamic private company La7. This study maps study results with observations made by the Osservatorio di Pavia, which is an Italian institute of research specialized in media analysis at theoretical and empirical level, engaged in the analysis of political communication in the mass media. This study takes also in account the data provided by Auditel regarding newscast audience, correlating the analysis of Social Media, of Facebook in particular, with measurable data, available to public domain.

IEEE Access, 2019

In recent times, news medias avail oneself of online social media platforms for news promotion, sharing and commentary to a large extent mainly in Twitter, Facebook, and Reddit. Therefore, in the literature, researchers have been used machine learning and text mining techniques to attain useful insights from the news media data in social media in-order to understand the factors for gaining large audience attention. Different to the previous studies, analyses of the news media in this work are based on a set of new features; content features such as the originality of a news item, context features such as time and circadian patterns of a news media, and reader reactions. Our dataset includes 238K tweets and 128K Facebook posts of 48 most popular news medias shared during May-June 2017. In this study we explored; news producers, news consumers, inter news production patterns, inter news dissemination behaviors, sharing similar news items within Twitter and Facebook (cross-posts), and news readers reactions on news items. In addition, we investigated the best time period to receive highest readers' attention towards their news items as this information is useful for other news medias to understand the best time duration to publish news items. Finally, we proposed a predictive model to increase news media popularity among readers and the results manifested that, a news media should disperse its own content and need to publish at first before other news media publish the same content in social media in-order to be popular and attract the attention from readers.

2019

Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019), 2020

The ubiquitous presence of social media is exerting its influence on several institutions in society by both positive and negative ways. In this context it behooves researchers to understand the validity of the influence of social media and what it means to the functionality of the institutions in society which include healthcare, politics, education, marriage, etc. This paper presents results and insights obtained from comparing sentiment analysis applied to Twitter and YouTube data on a set of topics. The focus of this study was to observe differences among sentiments expressed on different social media platforms. In other words, was there any influence generated by the social media platform on the individual's expression of sentiments. Additionally, we also developed an app to encourage citizen data scientists to search for a topic relevant to their area of interest and obtain sentiment analysis for that topic.

Digital Government: Research and Practice

A recent trend in political campaign studies is the use of sentiment analysis to understand users’ decisions. The scandal of Facebook and Cambridge Analytics is an example of efforts to use social media platforms to impact citizens’ will. This research aims to answer the question: Did the Facebook reactions of users in Mexico reflect the outcomes of the elections and possibly also the users’ emotions toward the political candidates of the State of Mexico in 2017? To answer the research question, we analyzed data collected from 4,128 Facebook posts and their reactions. The available reactions for Facebook users are: like, love, haha, wow, sad, and angry. Doing so revealed some kind of mood from the users in the Facebook comments section and opinions of the local government campaign in the central State of Mexico. The elections studied took place in June 2017. Our findings show that the winning political party had more negative sentiment and fewer posts and users’ discussions of the c...

ArXiv, 2019

Newspaper headlines contribute severely and have an influence on the social media. This work studies the durability of impact of verbs and adjectives on headlines and determine the factors which are responsible for its nature of influence on the social media. Each headline has been categorized into positive, negative or neutral based on its sentiment score. Initial results show that intensity of a sentiment nature is positively correlated with the social media impression. Additionally, verbs and adjectives show a relation with the sentiment scores

Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017

English. Different events and their reception in different reader communities may give rise to controversy. We propose a distant supervised entropy-based model that uses Facebook reactions as proxies for predicting news controversy. We prove the validity of this approach by running within-and across-source experiments, where different news sources are conceived to approximately correspond to different reader communities. Contextually, we also present and share an automatically generated corpus for controversy prediction in Italian. Italiano. Diversi tipi di eventi e la loro percezione in diverse comunità di utenti/lettori possono dare vita a controversie. In questo lavoro proponiamo un modello basato su entropia e sviluppato secondo il paradigma della "distant supervision" per predire controversie sulle notizie usando le reazioni di Facebook come "proxy". La validità dell'approcciò e dimostrata attraverso una serie di esperimenti usando dati provenienti dalla stessa fonte o da fonti diverse. Contestualmente, presentiamo anche un corpus generato automaticamente per la previsione delle controversie in italiano.

2013

The field of text sentiment analysis provides a unique indication of the electorate’s response towards political issues. The topic of social media campaigns has grown in interest as political decisions appear to have hung on these strategies of outreach to the electorate. We address this question by making use of a sentiment analysis lexicon, which specifically analyse microblog corpora, and statistical methods for temporal analysis. This approach was utilised to analyse the Facebook pages of the Leave campaign and the largest Remain advocacy group on social media for the UK-EU membership referendum of 2016, to test the hypothesis that sentiment is contagious through social media, and enquire if emotion acts as the backbone of the electorate’s decision. Our findings suggest that contrary to popular belief, the Leave campaign Facebook audience of Facebook followers became no more positive after the initial sentiment spike of the referendum results, and moreover sentiment polarized in the negative scale. Additionally, the sentiment expressed by the Leave campaign statuses was not significantly higher but was maintained more continuously in the lead up to the referendum date. The implications of these findings are that a successful political campaign through social media requires constant maintenance and coverage for followers to experience a continuous news-stream as described in the relevant literature. Lastly, we discuss the use of microblogging message content as a valid gauge of political sentiment and glean suggestions for further research.