A Survey of Location Prediction on Twitter Zheng, Xin; Han, Jialong; Sun, Aixin
IEEE transactions on knowledge and data engineering,
09/2018, Volume:
30, Issue:
9
Journal Article
Peer reviewed
Open access
Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned ...in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we make a conclusion of the survey and list future research directions.
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country ...to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.
•This paper reveals that related tweets failed to guide people on COVID-19 pandemic.•This study analyzes two types of tweets gathered during the pandemic times.•The research demonstrates that no useful words are found in WordCloud or word frequency in tweets.•Claims are validated by a proposed deep learning classifier model yielding accuracy up to 81%.•A designed Gaussian membership based fuzzy rule base correctly identifies sentiments from tweets.
In the past few years, Tweets have been widely used to perform Big Data analysis. However, the incredible amount of data captured by Twitter needs to be stored for further processing which may be a ...challenging task for many database systems. NoSQL is a generation of databases that aim to handle a large volume of data. However there is a large set of NoSQL systems, each has its own characteristics. Consequently choosing the suitable NoSQL system to handle Tweets is challenging. Based on these motivations, this work is carried out to find the suitable NoSQL system to manage Tweets. This paper presents the requirements of managing Tweets and provides a detailed comparison of five NoSQL systems namely, Redis, Cassandra, MongoDB, Couchbase and Neo4j regarding these requirements. The five NoSQL systems are compared in a real scenario where we collect and analyze 1.000.000 Tweets. The chosen scenario enables to evaluate not only the performance of the read and write operations, but also other requirements related to Tweets management such as scalability, analysis tools support and analysis languages support. The obtained results show that Couchbase is the most suitable NoSQL systems for managing Tweets.
•Tweets have a significant influence on people’s perceptions of corporations.•The Tweets can be used by corporations to influence people to think more positively about them, and hence.•Corporations ...can use Tweets to repair a damaged reputation.•The Tweet from the intelligent celebrity was more effective to repair reputational damage than those of the attractive celebrity.
These days, many corporations engage in Twitter activities as a part of their communication strategy. Corporations can use this medium to share information with stakeholders, to answer customer questions, or to build on their image. In this study we examined the extent to which celebrity Tweet messages can be used to repair a damaged corporate reputation, and how this message should be designed and what celebrity should be ‘used’.
In two experiments, a 2×2 (attractive celebrity versus intelligent celebrity)×(personal message versus general message) design was used. In total, 163 respondents first expressed their feelings regarding the two organisations in a baseline reputation measurement (M=4.72 on 7 point Likert scale). After that a news items was presented communicating a big fraud and mismanagement, resulting in a decreased reputation score (M=4.10). In the final stage one of the four experimental Tweets was presented, aimed at repairing the damaged reputation, which succeeded (M=4.43). For both organisations, the crisis prime significantly decreased reputation scores, and the Tweet significantly increased reputation score again. The analysis of variance shows a main effect for type of celebrity. In our experiment the intelligent celebrity’s Tweet was best to use.
The study reveals that celebrities’ Tweets can restore a positive public opinion about corporations. This study shows that when it comes to serious matters, an intelligent celebrity, who has the best fit with the topic, is of best impact. Consequences for corporate communication and future research are discussed.
Stance and Sentiment in Tweets Mohammad, Saif M.; Sobhani, Parinaz; Kiritchenko, Svetlana
ACM transactions on Internet technology,
08/2017, Volume:
17, Issue:
3
Journal Article
Peer reviewed
Open access
We can often detect from a person’s utterances whether he or she is in favor of or against a given target entity—one’s stance toward the target. However, a person may express the same stance toward a ...target by using negative or positive language. Here for the first time we present a dataset of tweet–target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.
COVID-19 Dey, Nilanjan; Mishra, Rishabh; Fong, Simon James ...
Digital government (New York, N.Y. Online),
01/2021, Volume:
2, Issue:
1
Journal Article
Nowadays, Climate change is an important environmental factor that affects every living thing on the earth. It is very essential to study the public perceptions regarding the disaster events ...frequently happening due to climate change. In today's digital era individuals are using social network platforms namely Twitter, Facebook, and Weibo now and then to express their views about any events. In this paper, the climate change Twitter data set was considered for analyzing the topics and the opinions discussed by the public regarding climate change. The Latent Dirichlet Allocation(LDA) method was used to list out the various topics present in the data set and the Bidirectional Encoder Representation from Transformers(BERT uncased) is an efficient deep learning technique used to classify the sentiments present in the data set. Here the sentiments were labelled as pro news, support, neutral and anti. The performance of the proposed topic modelling and sentiment classification model was compared using the precision, recall, and accuracy measures. The BERT uncased model with has shown the best results such as precision of 91.35%, recall of 89.65%, and accuracy of 93.50% compared to other methods.
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