Authenticity of Geo-Location and Place Name in Tweets

The place name and geo-coordinates of tweets are supposed to represent the possible location of the user at the time of posting that tweet. However, our analysis over a large collection of tweets indicates that these fields may not give the correct location of the user at the time of posting that tweet. Our investigation reveals that the tweets posted through third party applications such as Instagram or Swarmapp contain the geo-coordinate of the user specified location, not his current location. Any place name can be entered by a user to be displayed on a tweet. It may not be same as his/her exact location. Our analysis revealed that around 12% of tweets contains place names which are different from their real location. The findings of this research can be used as caution while designing location-based services using social media

A deep multi-modal neural network for informative Twitter content classification during emergencies

YesPeople start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents

Predicting the “helpfulness” of online consumer reviews

YesOnline shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites

Attention-based LSTM network for rumor veracity estimation of tweets

YesTwitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms

Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal

YesMobile payments are the future as we move towards a cashless society. In some markets, cash is already being replaced by digital transactions, but consumers of many developing countries are slower in transition towards digital payments. This study aims to identify major determinants of consumer mobile payment adoption in India the country with second largest mobile subscribers in the world. Existing mobile payments adoption studies have predominantly utilised Technology Acceptance Model (TAM), which was primarily developed in organisational context and criticised for having deterministic approach without much consideration for users’ individual characteristics. Therefore, this study adapted meta-UTAUT model with individual difference variable attitude as core construct and extended the model with consumer related constructs such as personal innovativeness, anxiety, trust, and grievance redressal. Empirical examination of the model among 491 Indian consumers revealed performance expectancy, intention to use, and grievance redressal as significant positive predictor of consumer use behaviour towards mobile payment. Moreover, intention to use was significantly influenced by attitude, social influence, and facilitating conditions. The major contribution of this study includes re-affirming the central role of attitude in consumer adoption studies and examining usage behaviour in contrast to most existing studies, which examine only behavioural intention

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

User Adoption of Cashless Services in Indian Context – A Preliminary Analysis

The recent measures adopted by the Government of India for the financial reforms in the country has led to an urgent need of different modes for cashless services. India is a country of diverse culture inhabited by people having a wide range of demographic factors. It is, thus, essential to identify the significant factors that affect the individual’s intention to adopt the cashless services. The main objective of this research is to analyse the users’ behaviour towards several constructs that have been identified as the potential enablers of the high rate of adoption of cashless services in India. The study builds upon the Unified Theory of Acceptance and Use of Technology model and adds several new factors that are significant in the Indian context. Results obtained through descriptive statistics suggest that the identified constructs are perceived significant by the users in shaping their intentions towards the use of cashless services