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It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule‐based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule‐based analysis.

The authors developed a java‐based data gathering tool with a rule‐based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces.

The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study shows that a historical event creates buzz on Twitter and people's interests on the event are reflected in their Twitter activity.

Applying data‐mining techniques to the social network data has so much potential. A possible improvement to the Twitter Data Collecting Tool would be an adaptation of a built‐in data‐mining module.

This paper focuses on designing a system handling massive amounts of Twitter Data. This is the first approach to embed a rule engine for filtering and analyzing social data. This paper will be valuable to those who may want to build their own Twitter dataset, apply customized filtering options to get rid of unnecessary, noisy data, and analyze social data to discover new knowledge.

The purpose of this paper is to use Twitter data to mine personality traits of basketball players to predict their performance in the National Basketball Association (NBA).

Automated personality mining and robotic process automation were used to gather data (player statistics and big five personality traits) of n = 185 professional basketball players. Correlation analysis and multiple linear regressions were computed to predict the performance of their NBA careers based on previous college performance and personality traits.

Automated personality mining of Tweets can be used to gather additional information about basketball players. Extraversion, agreeableness and conscientiousness correlate with basketball performance and can be used, in combination with previous game statistics, to predict future performance.

The study presents a novel approach to use automated personality mining of Twitter data as a predictor for future basketball performance. The contribution advances the understanding of the importance of personality for sports performance and the use of cognitive systems (automated personality mining) and the social media data for predictions. Scouts can use our findings to enhance their recruiting criteria in a multi-million dollar business, such as the NBA.

This paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through Arabic language and script, in vivo.

Using quantitative and qualitative situational linguistic analyses of Classical Arabic, found in Quranic and religious texts scripts; Modern Standard Arabic, which is commonly used in formal Arabic channels; and dialectical Arabic, which varies hugely from one Arabic country to another: this study analyses rich marketing and consumer messages (tweets) – as a basis for developing an Arabic language social media methodological tool.

Despite the popularity of Arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing Arabic social media conversations have limitations and require further development. Furthermore, due to its unique morphology, developing text understanding capabilities specific to the Arabic language poses challenges.

This study demonstrates the application and effectiveness of the proposed methodology on a random sample of Twitter data from Arabic-speaking regions. Furthermore, as Arabic is the language of Islam, the study is of particular importance to Islamic and Muslim geographies, markets and marketing.

The findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments.

To remedy these gaps, this study presents a new methodology and analytical approach to investigating Arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to estimate the political mood prevailing among the masses? Can they also be used to reliably predict the election outcome? To answer these in the Indian context, the 2014 general election was chosen.

Tweets posted on the leading parties during the voting and crucial campaign periods were mined and manual sentiment analysis was performed on them.

A strong and positive correlation was observed between the political sentiments expressed on Twitter and election results. Further, the Time Periods during which the tweets were mined were found to have a moderating effect on this relationship.

This study showed that the month preceding the voting period was the best to predict the vote share with Twitter data – with 83.9% accuracy.

Twitter has become an important public communication tool in India, and as the study results reinstate, it is an ideal research tool to gauge public opinion.

The main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.

In order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).

The authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.

Extremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.

This study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.

The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India.

The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India.

The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India.

The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue.

Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

This paper investigates the use of Twitter for studying the social representations of different regions across the world towards new food trends.

A density-based clustering algorithm was applied to 7,014 tweets to identify regions of consumers sharing content about food trends. The attitude of their social representations was addressed with the sentiment analysis, and grid maps were used to explore subregional differences.

Twitter users have a weak, positive attitude towards food trends, and significant differences were found across regions identified, which suggests that factors at the regional level such as cultural context determine users' attitude towards food innovations. The subregional analysis showed differences at the local level, which reinforces the evidence that context matters in consumers' attitude expressed in social media.

The social media content is sensitive to spatio-temporal events. Therefore, research should take into account content, location and contextual information to understand consumers' perceptions. The methodology proposed here serves to identify consumers' regions and to characterize their attitude towards specific topics. It considers not only administrative but also cognitive boundaries in order to analyse subsequent contextual influences on consumers' social representations.

The approach presented allows marketers to identify regions of interest and localize consumers' attitudes towards their products using social media data, providing real-time information to contrast with their strategies in different areas and adapt them to consumers' feelings.

This study presents a research methodology to analyse food consumers' understanding and perceptions using not only content but also geographical information of social media data, which provides a means to extract more information than the content analysis applied in the literature.

This paper discusses how the features of blockchain technology impact supply chain transparency through the lens of the information security triad (confidentiality, integrity and availability). Ultimately, propositions are developed to encourage future research in supply chain applications of blockchain technology.

Propositions are developed based on a synthesis of the information security and supply chain transparency literature. Findings from text mining of Twitter data and a discussion of three major blockchain use cases support the development of the propositions.

The authors note that confidentiality limits supply chain transparency, which causes tension between transparency and security. Integrity and availability promote supply chain transparency. Blockchain features can preserve security and increase transparency at the same time, despite the tension between confidentiality and transparency.

The research was conducted at a time when most blockchain applications were still in pilot stages. The propositions developed should therefore be revisited as blockchain applications become more widely adopted and mature.

This study is among the first to examine the way blockchain technology eases the tension between supply chain transparency and security. Unlike other studies that have suggested only positive impacts of blockchain technology on transparency, this study demonstrates that blockchain features can influence transparency both positively and negatively.

The purpose of this study is to examine the emergences of social media such as Facebook, Twitter and Instagram have changed the way human beings communicate and interact. In the past few years, this has become crucial in the context of business, especially in start-up fund raising. Access to venture capital financing is a crucial issue in the entrepreneurial finance literature. To further explore the use of social media for entrepreneurs, the authors have explored how entrepreneurs use social media for fund-raising purposes. The authors have used Application Programming Interfaces (APIs) to collect entrepreneurs’ funding data from Crunchbase and entrepreneurs’ social media data from Facebook and Twitter. The results show that social media is significant for start-ups in their success or failure in fund raising. Investing energy into utilizing online social media and exhausting these platforms consciously contributes to the financial success of start-ups. Therefore, start-ups which are popular among online fans and followers can manage to raise larger amounts of funding in the early stages.

This research relies on a wide range of quantitative data, which was obtained from three different online sources which includes Facebook, Twitter and CrunchBase. The use of a variety of internet technologies have been linked to increases in individuals’ social network diversity, which likely increases access to social capital at the individual level (Hampton and Wellman, 2003). The dataset was retrieved by using APIs, which enables the collection of novel metrics, from various sources that provide a well-structured dataset (Priem and Hemminger, 2010). Hypotheses were tested on a longitudinal dataset from 2000 to 2013, comprising general and investment data and social media metrics of start-ups. First, a sample from the database was selected to ensure data availability and reliability. After sampling, all the selected companies’ Twitter and Facebook activities were observed and metrics were analysed. SPSS was used to conduct correlation and regression analyses.

This study analysed whether start-ups’ social media convention is able to influence investors’ choices, especially the amount of total funding given. The paper showed that innovative start-up companies were able to benefit from communicating on social media platforms. Start-ups, which were using Facebook and Twitter effectively, focusing on valuable social media metrics, received larger amount of funding in total. Furthermore, it was observed that as their business grew, they intended to put more effort into online social networking. It confirmed the idea that businesses are using social media consciously.

This is the only paper that the authors could find that examines the relationship between fundraising and activity on social networks.