Collaborative Inference of Coexisting Information Diffusions

Recently, \textit{diffusion history inference} has become an emerging research topic due to its great benefits for various applications, whose purpose is to reconstruct the missing histories of information diffusion traces according to incomplete observations. The existing methods, however, often focus only on single information diffusion trace, while in a real-world social network, there often coexist multiple information diffusions over the same network. In this paper, we propose a novel approach called Collaborative Inference Model (CIM) for the problem of the inference of coexisting information diffusions. By exploiting the synergism between the coexisting information diffusions, CIM holistically models multiple information diffusions as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without any prior assumption of diffusion models, and collaboratively infers the histories of the coexisting information diffusions via a low-rank approximation of CDT with a fusion of heterogeneous constraints generated from additional data sources. To improve the efficiency, we further propose an optimal algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA), which can speed up the inference without compromise on the accuracy by utilizing the temporal locality of information diffusions. The extensive experiments conducted on real world datasets and synthetic datasets verify the effectiveness and efficiency of CIM and TWPDA

Spiteful, one-off, and kind: Predicting customer feedback behavior on Twitter

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Public Opinion Analysis of the Transportation Policy Using Social Media Data: A Case Study on the Delhi Odd–Even Policy

Twitter, a microblogging service, has become a popular platform for people to express their views and opinions on different issues. A sentiment analysis of the tweets can help in understanding the public opinion on different government decisions. This paper used Twitter data to extract the sentiments of people during the Phase 1 and Phase 2 of the odd–even policy implemented by the Delhi government to curb the air pollution and improve traffic flow. In this study, we used four different lexicon-based approaches: Bing, Afinn, National Research Council emotion lexicon, and Deep Recursive Neural Network-based Natural Language Processing software (CoreNLP) to extract sentiments from tweets and thereby assess overall public opinions. The daily trend obtained for each phase was normalized with the number of tweets and then compared using the Granger causality test. The causality test results showed that the trends obtained during the two phases were significantly different from each other. In particular, public sentiments were found to mostly turn negative during the later stage of the Phase 2 which indicates fading away of the public enthusiasm and positiveness towards the policy during the later stages of the policy implementation

The Effects of Gender Signals and Performance in Online Product Reviews

This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed next to products. Differences in reviews, perceived - consciously or unconsciously - with respect to gender signals, can lead to crucial biases in determining what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names to select reviews where the author’s likely gender can be inferred. Using reviews authored by these gender-signaling authors, we train a deep learning classifier to quantify the gendered writing style (i.e., gendered performance) of reviews written by authors who do not send clear gender signals via their user name. We contrast the effects of gender signaling and performance on the review helpfulness ratings using matching experiments. This is aimed at understanding if an advantage is to be gained by (not) signaling one's gender when posting reviews. While we find no general trend that gendered signals or performances influence overall review success, we find strong context-specific effects. For example, reviews in product categories such as Electronics or Computers are perceived as less helpful when authors signal that they are likely woman, but are received as more helpful in categories such as Beauty or Clothing. In addition to these interesting findings, we believe this general chain of tools could be deployed across various social media platforms

A Quantitative Approach to Evaluate and Develop Theories on (Fear of) Crime in Urban Environments

Well established work in criminological, architectural and urban studies suggests that there is a strong correlation between crime, perceived safety, the fear of crime, and the presence of different demographics, the people dynamics, in an urban environment. These studies have been conducted primarily using qualitative evaluation methods, and are typically limited in terms of the geographical area they cover, the number of respondents they reach out to, and the temporal frequency with which they can be repeated. As cities are rapidly growing and evolving complex entities, complementary approaches that afford social and urban scientists the ability to evaluate urban crime and fear of crime theories at scale are required. In this thesis, I propose a combination of methodologies following a data mining and crowdsourcing approach to quantitatively validate these theories at scale, and to support the exploration of new ones. To relate people dynamics to crime quantitatively, I first analyse footfall counts as recorded by telecommunication data, and extract metrics that act as proxies of urban crime theories. Using correlation and regression analysis between such proxies and crime activity derived from open crime data records, the method can help to understand to what extent different theories of urban crime hold, and where. To relate people dynamics to fear of crime quantitatively, I then built two image– based online crowdsourcing platforms to investigate to what extent online crowdsourcing can be used to gather safety perceptions about urban places, defined by the combination of built environment and the people inhabiting it. As existing theories suggest that knowing who the respondents are is crucial for understanding safety perceptions, I also gathered their demographic background information to discuss their perceptions accordingly. I applied analysis of variance (ANOVA) and covariance (ANCOVA) to these data. The method can help to understand what visual properties based on peopl

Analyzing the Overturning of Roe vs Wade on Twitter using Natural Language Processing Techniques

In 1973, the historic U.S. Supreme Court (SCOTUS) case of Roe vs. Wade provided the constitutional rightto abortion. However, on May 2, 2022, Politico magazine leaked the draft opinion on the Dobbs v. Jackson Women’s Health Organization. The leak generated a surge of users to post their opinion on the case that would eliminate abortion as a constitutional right. Then, on June 24, 2022, SCOTUS overturned Roe vs. Wade. In this thesis, we aim to investigate the public opinion and reaction towards the overturning of Roe vs. Wade. We collected 20,640,166 tweets using Twitter API for Academic Research and an open-sourced dataset published during two periods. The first period was a week before Politico magazine leaked theSCOTUS decision and the week after. The second period was a week before and over a week after theoverturning of Roe vs. Wade. Using natural language processing techniques, including sentiment analysis,emotion recognition, topic modeling, and bi-grams, we could develop insight into public opinion based onthe posted tweets. Our research investigates if there is a change in sentiment over time, a change in theemotion expressed within the text over time, and which topics are most common within the collection oftweets. The results demonstrate a significant increase on the day of the Politico leak, which showed thatmost of the tweets published on that day expressed a positive sentiment. However, in the weeks before andafter the overturning of Roe v. Wade, we witness a decrease from the beginning of the period up to the dayof the overturn. Regarding emotion recognition, there is a significant decrease in the proportion of tweetsclassified as expressing optimism. There’s also an increase in the proportion of tweets expressing anger when comparing the day of the Politico leak and the day of the overturn. The topic model we applied to thetweets published on the day of the Politico leak revealed that states’ rights and children were discussed. Using bigram of the most negative tweets, we witnessed gun control and healthcare as words that frequently occurred within the collection