Search results for: Twitter

Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Melissa Vang, Raheyma Khan, Haihua Chen

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The coronavirus disease 2019 (COVID‐19) pandemic has changed people's lives from all over the world. More people have turned to Twitter to engage online and discuss the COVID-19 vaccine. This study aims to present a text mining approach to identify people's attitudes towards the COVID-19 vaccine on Twitter. To achieve this purpose, we collected 54,268 COVID-19 vaccine tweets from September 01, 2020, to November 01, 2020, then the BERT model is used for the sentiment and topic analysis. The results show that people had more negative than positive attitudes about the vaccine, and countries with an increasing number of confirmed cases had a higher percentage of negative attitudes. Additionally, the topics discussed in positive and negative tweets are different. The tweet datasets can be helpful to information professionals to inform the public about vaccine-related informational resources. Our findings may have implications for understanding people's cognitions and feelings about the vaccine.

Keywords: BERT, COVID-19 vaccine, sentiment analysis, topic modeling

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Authors: García López

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The Government Departments of Health have the task of informing and educating citizens about public health issues. For this, they use channels like Twitter, key in the search for health information and the propagation of content. The tweets, important in the virality of the content, may contain emotions that influence the contagion and exchange of knowledge. The goal of this study is to perform an analysis of the emotional projection of health information shared on Twitter by official American accounts: the disease control account CDCgov, National Institutes of Health, NIH, the government agency HHSGov, and the professional organization PublicHealth. For this, we used Tone Analyzer, an International Business Machines Corporation (IBM) tool specialized in emotion detection in text, corresponding to the categorical model of emotion representation. For 15 days, all tweets from these accounts were analyzed with the emotional analysis tool in text. The results showed that their tweets contain an important emotional load, a determining factor in the success of their communications. This exposes that official accounts also use subjective language and contain emotions. The predominance of emotion joy over sadness and the strong presence of emotions in their tweets stimulate the virality of content, a key in the work of informing that government health departments have.

Keywords: emotions in tweets, emotion detection in the text, health information on Twitter, American health official accounts, emotions on Twitter, emotions and content

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Authors: Najiba Ouled Omar, Azza Harbaoui, Henda Ben Ghezala

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Current research practices sentiment analysis with a focus on social networks, DEfi Fouille de Texte (DEFT) (Text Mining Challenge) evaluation campaign focuses on opinion mining and sentiment analysis on social networks, especially social network Twitter. It aims to confront the systems produced by several teams from public and private research laboratories. DEFT offers participants the opportunity to work on regularly renewed themes and proposes to work on opinion mining in several editions. The purpose of this article is to scrutinize and analyze the works relating to opinions mining and sentiment analysis in the Twitter social network realized by DEFT. It examines the tasks proposed by the organizers of the challenge and the methods used by the participants.

Keywords: opinion mining, sentiment analysis, emotion, polarity, annotation, OSEE, figurative language, DEFT, Twitter, Tweet

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Authors: Thomai Voulgari, Vasilis Vasilopoulos, Antonis Skamnakis

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The aim of this study is to investigate the effect of the European election campaigns (May 23-26, 2019) on Twitter achieving with artificial intelligence tools such as troll factories and automated inauthentic accounts. Our research focuses on the last European Parliamentary elections that took place between 23 and 26 May 2019 specifically in Italy, Greece, Germany and France. It is difficult to estimate how many Twitter users are actually bots (Echeverría, 2017). Detection for fake accounts is becoming even more complicated as AI bots are made more advanced. A political bot can be programmed to post comments on a Twitter account for a political candidate, target journalists with manipulated content or engage with politicians and artificially increase their impact and popularity. We analyze variables related to 1) the scope of activity of automated bots accounts and 2) degree of coherence and 3) degree of interaction taking into account different factors, such as the type of content of Twitter messages and their intentions, as well as the spreading to the general public. For this purpose, we collected large volumes of Twitter accounts of party leaders and MEP candidates between 10th of May and 26th of July based on content analysis of tweets based on hashtags while using an innovative network analysis tool known as MediaWatch.io (https://mediawatch.io/). According to our findings, one of the highest percentage (64.6%) of automated “bot” accounts during 2019 European election campaigns was in Greece. In general terms, political bots aim to proliferation of misinformation on social media. Targeting voters is a way that it can be achieved contribute to social media manipulation. We found that political parties and individual politicians create and promote purposeful content on Twitter using algorithmic tools. Based on this analysis, online political advertising play an important role to the process of spreading misinformation during elections campaigns. Overall, inauthentic accounts and social media algorithms are being used to manipulate political behavior and public opinion.

Keywords: artificial intelligence tools, human-bot interactions, political manipulation, social networking, troll factories

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Authors: R. Sudhesh Solomon, P. Y. K. L. Srinivas, Abhay Narayan, Amitava Das

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Users in social networks either unicast or broadcast their messages. At mention is the popular way of unicasting for Twitter whereas general tweeting could be considered as broadcasting method. Understanding the information flow and dynamics within a Social Network and modeling the same is a promising and an open research area called Information Diffusion. This paper seeks an answer to a fundamental question - understanding if the at-mention network or the unicasting pattern in social media is purely random in nature or is there any user specific selectional preference? To answer the question we present an empirical analysis to understand the sociological aspects of Twitter mentions network within a social network community. To understand the sociological behavior we analyze the values (Schwartz model: Achievement, Benevolence, Conformity, Hedonism, Power, Security, Self-Direction, Stimulation, Traditional and Universalism) of all the users. Empirical results suggest that values traits are indeed salient cue to understand how the mention-based communication network functions. For example, we notice that individuals possessing similar values unicast among themselves more often than with other value type people. We also observe that traditional and self-directed people do not maintain very close relationship in the network with the people of different values traits.

Keywords: information diffusion, personality and values, social network analysis, twitter mentions network

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Authors: Elcin Timur Cakmak, Ayse Oguzlar

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This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Adel Assiri, Ahmed Emam, Hmood Al-Dossari

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Sentiment analysis (SA) has received growing attention in Arabic language research. However, few studies have yet to directly apply SA to Arabic due to lack of a publicly available dataset for this language. This paper partially bridges this gap due to its focus on one of the Arabic dialects which is the Saudi dialect. This paper presents annotated data set of 4700 for Saudi dialect sentiment analysis with (K= 0.807). Our next work is to extend this corpus and creation a large-scale lexicon for Saudi dialect from the corpus.

Keywords: Arabic, sentiment analysis, Twitter, annotation

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Authors: Noor Farizah Ibrahim, Christopher Durugbo

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This study aims to investigate how the Twittersphere reacted during the recent historical event of robotic landing on a comet. The news is about Philae, a robotic lander from European Space Agency (ESA), which successfully made the first-ever rendezvous and touchdown of its kind on a nucleus comet on November 12, 2014. In order to understand how Twitter is practically used in spreading messages on historical events, we conducted an analysis of one-week tweet feeds that contain the #CometLanding hashtag. We studied the trends of tweets, the diffusion of the information and the characteristics of the social network created. The results indicated that the use of Twitter as a platform enables online communities to engage and spread the historical event through social media network (e.g. tweets, retweets, mentions and replies). In addition, it was found that comprehensible and understandable hashtags could influence users to follow the same tweet stream compared to other laborious hashtags which were difficult to understand by users in online communities.

Keywords: diffusion of information, hashtag, social media, Twitter

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Authors: Anas Alsuhaibani

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Observing debate and discussion over social media has been found to be a promising tool to investigate different types of opinion. On 23 June 2016, Brexit voters in the UK decided to depart from the EU, with 51.9% voting to leave. On Twitter, there had been a massive debate in this context, and the hashtag Brexit was allocated as number six of the most tweeted hashtags across the globe in 2016. The study aimed to investigate the sentiment and themes expressed in a sample of tweets during a political event (Brexit) in 2016. A sentiment and thematic analysis was conducted on 1304 randomly selected tweets tagged with the hashtag Brexit in Twitter for the period from 10 June 2016 to 7 July 2016. The data were coded manually into two code frames, sentiment and thematic, and the reliability of coding was assessed for both codes. The sentiment analysis of the selected sample found that 45.63% of tweets conveyed negative emotions while there were only 10.43% conveyed positive emotions. It also surprisingly resulted that 29.37% were factual tweets, where the tweeter expressed no sentiment and the tweet conveyed a fact. For the thematic analysis, the economic theme dominated by 23.41%, and almost half of its discussion was related to business within the UK and the UK and global stock markets. The study reported that the current UK government and relation to campaign themes were the most negative themes. Both sentiment and thematic analyses found that tweets with more than one opinion or theme were rare, 8.29% and 6.13%, respectively.

Keywords: Brexit, political opinion mining, social media, twitter

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Authors: Yaser Mohammed Altameemi

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This research looks at the representation of the social media for the Saudi Government decrees regarding the developmental projects of the Saudi 2030 vision. The paper analyses a television interview with the Crown Prince Mohammed Bin Salman who talks about the progress of the Saudi vision of 2030, and how the government had acted as response to the COVID-19 pandemic. The interview was on 28/4/2021. The paper analyses the tweets on Twitter that cover the interview for the purpose of investigating the development of concepts and meanings regarding the Saudi peoples’ orientations towards the Saudi projects. The data include all related tweets from the day of the interview and the following seven days after the interview. The finding of the collocation analysis suggests that nationalism notion is explicitly expressed by users in Twitter. The main finding of this paper suggests the importance of further analyses for the concordance lines. However, the collocation network suggests that there is a clear highlight for nationalism.

Keywords: social media, twitter, political interview, prince Mohammed Bin Salman, Saudi vision 2030

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Authors: Sultan Ali Suleiman AlMazrouei

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Purpose: The purpose of this paper is to examine the effect of communication through social networking sites (Facebook, Twitter) on employee engagement. Methodology: A quantitative survey was used to collect data from 440 employees from the Ministry of Education in Oman. SPSS software was used to analyze the data. Findings: The results revealed a positive significant relationship between communication via Facebook and employee engagement. However, communication via Twitter does not influence employee engagement significantly. Practical implications: Managers can benefit from the study by understanding the importance of communication via Facebook with employees in order to increase their engagement. They should post their views and thoughts on Facebook and encourage their employees to be members which would be reflected on their psychological side positively. That gives them a feeling of belonging to a network. Originality/value: The study enriches the human resources management literature by examining a theoretical framework about the influence of social networking sites usage on employee engagement. This is one of the few studies that focus on the relationship of social networking sites usage with employees' engagement. It is the first study in an Omani context.

Keywords: employee engagement, social networking sites, Facebook, Twitter

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Authors: Mary-Magdalene N. Chumbow

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This study explores how a reality TV show which aired in Nigeria in 2019 (Big Brother Naija - BBN), played a role in enhancing gender-biased conversations among its viewers and social media followers. Thematic analysis is employed here to study Twitter conversations among BBN 2019 followers, which ensued after the show had stopped airing. The study reveals that the show influenced the way viewers and fans engaged with each other, as well as with the show’s participants, on Twitter, and argues that, despite having aired for a short period of time, BBN 2019 was able to draw people together and provide a community where viewers could engage with each other online. Though the show aired on TV, the viewers found a digital space where they could air their views, react to what was happening on the show, as well as simply catch up on action that they probably missed. Within these digital communities, viewers expressed their attractions, disgust and identities, most of these having a form of reference to sexuality and gender identities and roles, as were also portrayed by the show’s producers both on TV and on social media.

Keywords: commodification of bodies, gender stereotypes, Big Brother Naija, social media

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Authors: Gregory D. Saxton

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Networks are everywhere: lunch ties among co-workers, golfing partnerships among employees, interlocking board-of-director connections, Facebook friendship ties, etc. Each network varies in terms of its structure -its size, how inter-connected network members are, and the prevalence of sub-groups and cliques. At the same time, within any given network, some network members will have a more important, more central position on account of their greater number of connections or their capacity as “bridges” connecting members of different network cliques. The logic of network structure and position is at the heart of what is known as social network analysis, and this paper applies this logic to the study of the stock market. Using an array of data analytics and machine learning tools, this study will examine 17 million Twitter messages discussing the stocks of the firms in the S&P 1,500 index in 2018. Each of these 1,500 stocks has a distinct Twitter discussion network that varies in terms of core network characteristics such as size, density, influence, norms and values, level of activity, and embedded resources. The study’s core proposition is that the ultimate effect of any market-relevant information is contingent on the characteristics of the network through which it flows. To test this proposition, this study operationalizes each of the core network characteristics and examines their influence on market reactions to 2018 quarterly earnings announcement events.

Keywords: data analytics, investor-to-investor communication, social network analysis, Twitter

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Authors: Ashwaq Alsulami, Jianhua Shao

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Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.

Keywords: attributed community, attribute detection, community, social network

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Authors: Taoues Hadour

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Twitter has become a popular means of communication used in a variety of fields, such as politics, journalism, and academia. This widely used online platform has an impact on the way people express themselves and is changing language usage worldwide at an unprecedented pace. The language used online reflects the linguistic battle that has been going on for several decades in French society. This study enables a deeper understanding of users' linguistic behavior online. The implications are important and allow for a rise in awareness of intercultural and cross-language exchanges. This project investigates the mixing of French-English language usage among French users of Twitter using a topic analysis approach. This analysis draws on Gumperz's theory of conversational switching. In order to collect tweets at a large scale, the data was collected in R using the rtweet package to access and retrieve French tweets data through Twitter’s REST and stream APIs (Application Program Interface) using the software RStudio, the integrated development environment for R. The dataset was filtered manually and certain repetitions of themes were observed. A total of nine topic categories were identified and analyzed in this study: entertainment, internet/social media, events/community, politics/news, sports, sex/pornography, innovation/technology, fashion/make up, and business. The study reveals that entertainment is the most frequent topic discussed on Twitter. Entertainment includes movies, music, games, and books. Anglicisms such as trailer, spoil, and live are identified in the data. Change in language usage is inevitable and is a natural result of linguistic interactions. The use of different languages online is just an example of what the real world would look like without linguistic regulations. Social media reveals a multicultural and multilinguistic richness which can deepen and expand our understanding of contemporary human attitudes.

Keywords: code-switching, French, sociolinguistics, Twitter

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Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Jorge Cortés, Alejandra Martínez, Carlos Pérez, Anaid Simón

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The presence of government, news media, and general citizenry in social media allows considering interactions between them as a form of political communication (i.e. the public exchange of contradictory discourses about politics). Twitter’s asymmetrical following model (users can follow, mention or reply to other users that do not follow them) could foster alternative democratic practices and have an impact on Mexican political culture, which has been marked by a lack of direct communication channels between these actors. The research aim is to assess Twitter’s role in political communication practices through the analysis of interaction dynamics between government, news media, and citizens by extracting and visualizing data from Twitter’s API to observe general behavior patterns. The hypothesis is that regardless the fact that Twitter’s features enable direct and horizontal interactions between actors, users repeat traditional dynamics of interaction, without taking full advantage of the possibilities of this medium. Through an interdisciplinary team including Communication Strategies, Information Design, and Interaction Systems, the activity on Twitter generated by the controversy over the presence of Uber in Mexico City was analysed; an issue of public interest, involving aspects such as public opinion, economic interests and a legal dimension. This research includes techniques from social network analysis (SNA), a methodological approach focused on the comprehension of the relationships between actors through the visual representation and measurement of network characteristics. The analysis of the Uber event comprised data extraction, data categorization, corpus construction, corpus visualization and analysis. On the recovery stage TAGS, a Google Sheet template, was used to extract tweets that included the hashtags #UberSeQueda and #UberSeVa, posts containing the string Uber and tweets directed to @uber_mx. Using scripts written in Python, the data was filtered, discarding tweets with no interaction (replies, retweets or mentions) and locations outside of México. Considerations regarding bots and the omission of anecdotal posts were also taken into account. The utility of graphs to observe interactions of political communication in general was confirmed by the analysis of visualizations generated with programs such as Gephi and NodeXL. However, some aspects require improvements to obtain more useful visual representations for this type of research. For example, link¬crossings complicates following the direction of an interaction forcing users to manipulate the graph to see it clearly. It was concluded that some practices prevalent in political communication in Mexico are replicated in Twitter. Media actors tend to group together instead of interact with others. The political system tends to tweet as an advertising strategy rather than to generate dialogue. However, some actors were identified as bridges establishing communication between the three spheres, generating a more democratic exercise and taking advantage of Twitter’s possibilities. Although interactions in Twitter could become an alternative to political communication, this potential depends on the intentions of the participants and to what extent they are aiming for collaborative and direct communications. Further research is needed to get a deeper understanding on the political behavior of Twitter users and the possibilities of SNA for its analysis.

Keywords: interaction, political communication, social network analysis, Twitter

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Authors: Mai Alshareef

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The Saudi government considers the modernisation of the education system as a critical component of the national development plan, Saudi Vision 2030; however, this sudden reform creates tension amongst Saudis. This study examines first the reflection of the social and digital education reform on stakeholders and the general Saudi public, and second, the influence of information and communication technologies (ICTs) on the ethnoreligious conflict in Saudi Arabia. This study employs Cyberconflict theory to examine conflicts in the real world and cyberspace. The findings are based on a qualitative case study methodology that uses netnography, an analysis of 3,750 Twitter posts and semi-structural interviews with 30 individuals, including key actors in the Saudi education sector and Twitter activists during 2019\2020. The methods utilised are guided by thematic analysis to map an understanding of factors that influence societal conflicts in Saudi Arabia, which in this case include religious, national, and gender identity. Elements of Cyberconflict theory are used to better understand how conflicting groups build their identities in connection to their ethnic/religious/cultural differences and competing national identities. The findings correspond to the ethnoreligious components of the Cyberconflict theory. Twitter became a battleground for liberals, conservatives, the Saudi public and elites, and it is used in a novel way to influence public opinion and to challenge the media monopoly. Opposing groups relied heavily on a discourse of exclusion and inclusion and showed ethnic and religious affiliations, national identity, and chauvinism. The findings add to existing knowledge in the cyberconflict field of study, and they also reveal outcomes that are critical to the Saudi Arabian national context.

Keywords: education, cyberconflict, Twitter, national identity

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Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

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This study is conducted to examine how opposing viewpoints related to COVID-19 were diffused on Twitter. To accomplish this, six datasets using two epidemiological models, SIR (Susceptible, Infected, Recovered) and SEIZ (Susceptible, Exposed, Infected, Skeptics), were analyzed. The six datasets were chosen because they represent opposing viewpoints on the COVID-19 pandemic. Three of the datasets contain anti-subject hashtags, while the other three contain pro-subject hashtags. The time frame for all datasets is three years, starting from January 2020 to December 2022. The findings revealed that while both models were effective in evaluating the propagation trends of these polarizing viewpoints, the SEIZ model was more accurate with a relatively lower error rate (6.7%) compared to the SIR model (17.3%). Additionally, the relative error for both models was lower for anti-subject hashtags compared to pro-subject hashtags. By leveraging epidemiological models, insights into the propagation trends of polarizing viewpoints on Twitter were gained. This study paves the way for the development of methods to prevent the spread of ideas that lack scientific evidence while promoting the dissemination of scientifically backed ideas.

Keywords: mathematical modeling, epidemiological model, seiz model, sir model, covid-19, twitter, social network analysis, social contagion

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Rebecca Cervantes, Elishah Maro Pangilinan

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The current research used Uses and gratifications theory to further understand the motivations and satisfaction students get from Facebook and Twitter. The researchers relate the objectives in developing uses and gratifications theory 1) to explain how individuals use mass communication to gratify their needs, “what do people do with the media” many of these young adults use social media networks to communicate with family, friends, and even strangers. Social media sites have created new and non-personal ways for people to interact with others and young adults have taken advantage of this technological trend; 2) to discover underlying motives for individuals’ media use 3) to identify the positive and the negative consequences of individual media use. The researchers use survey questionnaires to gather information that is used in this study. A descriptive analysis was used to measure the answers to a 24-item questionnaire.

Keywords: adolescent, communication, social media, #Hashtag

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Authors: Chaohong Pan

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Through public diplomacy on social media, governments have attempted to influence foreign public opinion. What is the impact of digital public diplomacy? Public diplomacy research often relies on content analysis to study the strategies employed by communicators but has rarely examined its actual impact on the audience. In addition, we do not know if giving a communicator an explicit label, as Twitter does with “government account”, would change the effects of the messages. Can the government label reduce the percussiveness of public diplomacy messages by sending a warning signal? Using a 2 × 2 survey experiment, the present paper contributes to the study of public diplomacy by randomly exposing American participants to four types of tweets from Chinese diplomats. The stimulus materials vary in terms of the tweets’ content (“positive-china” vs. “negative-US) and Twitter government labels (with vs. without the labels). I found that positive tweets about China have a significant positive effect on Americans’ attitudes toward China, whereas negative tweets about the US have little effect on their opinions. Furthermore, positive-China tweets are effective only on China-related issues, which indicates that Chinese diplomats’ tweets have limited effects on shaping a foreign audience’s attitudes toward their own country. Lastly, I find that labels largely have no impact on a diplomatic tweet’s effect. These results contribute to our understanding of the effects of public diplomacy in the digital age.

Keywords: public diplomacy, china, foreign public opinion, twitter

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Maryam Esfandiari, Bohdan Fridrich

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On December 28, 2017, a wave of protests erupted in several Iranian cities. Protesters demonstrated against the president, Hasan Rohani, and theocratical nature of the regime. Iran has a recent history with protest movements, such as Green Movement responsible for demonstrations after 2009 Iranian presidential election. However, the 2017/2018 protests differ from the previous ones in terms of organization and agenda. The events show little to no central organization and seem as being sparked by grass root movements and by citizens’ fatigue of government corruption, authoritarianism, and economic problems of the country. Social media has played important role in communicating the protests to the outside world and also in general coordination. By using content analyses, this paper analyzes the visual content of Twitter posts published during the protests. It aims to find the correlation between their decentralized nature and nature of the tweets – either emotionally arousing or efficiency-elicit. Pictures are searched by hashtags and coded by their content, such as ‘crowds,’ ‘protest activities,’ ‘symbols of unity,’ ‘violence,’ ‘iconic figures,’ etc. The study determines what type of content prevails and what type is the most impactful in terms of reach. This study contributes to understanding the role of social media both as a tool and a space in protest organization and portrayal in countries with limited Internet access.

Keywords: twitter, Iran, collective action, protest

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Authors: Moulana Mohammed

Abstract:

Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.

Keywords: interactive visualization, visual mon-negative matrix factorization model, optimal number of topics, cluster validity indices, Twitter data clustering

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Authors: Josiel Delgadillo, Johnson Kinyua, Charles Mutigwe

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The purpose of sentiment analysis is to determine the sentiment strength (e.g., positive, negative, neutral) from a textual source for good decision-making. Natural language processing in domains such as financial markets requires knowledge of domain ontology, and pre-trained language models, such as BERT, have made significant breakthroughs in various NLP tasks by training on large-scale un-labeled generic corpora such as Wikipedia. However, sentiment analysis is a strong domain-dependent task. The rapid growth of social media has given users a platform to share their experiences and views about products, services, and processes, including financial markets. StockTwits and Twitter are social networks that allow the public to express their sentiments in real time. Hence, leveraging the success of unsupervised pre-training and a large amount of financial text available on social media platforms could potentially benefit a wide range of financial applications. This work is focused on sentiment analysis using social media text on platforms such as StockTwits and Twitter. To meet this need, SkyBERT, a domain-specific language model pre-trained and fine-tuned on financial corpora, has been developed. The results show that SkyBERT outperforms current state-of-the-art models in financial sentiment analysis. Extensive experimental results demonstrate the effectiveness and robustness of SkyBERT.

Keywords: BERT, financial markets, Twitter, sentiment analysis

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Authors: Djehich Mohamed Yousri

Abstract:

With the advent of social media, political communication scholars have systematically reviewed theories and empirical findings that revolve around media use and democracy. It is interesting that around the same time period, there has been a trend towards revitalization of political populism in different latitudes around the world. This wide-ranging populist movement has expanded regardless of whether these political systems are established democracies, emerging democracies, or societies mired in endangered political contexts. This article serves as an introductory piece to a special issue on populism. First, it highlights the ways in which "populism", as an ancient phenomenon, has transmigrated into the political sphere in the age of social media. Second, the article seeks to better define the populist context and how it has evolved in today's hybrid media society. Finally, this introduction also lays the groundwork for six data-driven theoretical core papers that cover many of the important issues revolving around the phenomenon of populism today.

Keywords: democracy, facebook, populism, social media, twitter

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Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

Abstract:

Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

Procedia PDF Downloads 446