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The abundance and real-time availability of Twitter data have proved beneficial in detecting events in various domains such as emergency situations, crime detection, public health, place recommendations, etc. Nevertheless, two critical challenges occur while detecting events using social media data. First, the uncertainty in capturing the contextual relationship among tweets, which is the result of the limited availability of the contextual information due to the small length of tweets. Second, the high computation cost required in event detection due to massive data processing. Earlier research works, addressing these challenges, have tried to capture the contextual information by using the dense vector representations of texts leveraging deep neural word embedding generation models such as Word2Vec and GloVe. However, these models are trained on the Euclidean vector space which fails to amalgamate the directional information of the vectors with the semantic information in text, incurring high computational costs. To target both the problems simultaneously, we propose modeling Twitter data as a graph-of-sentences which retains the contextual relationships while maintaining lower computational cost. The proposed model captures contextual information using JoSE, a spherical vector representation leveraging the word-word and word-paragraph semantic co-occurrence statistics in a spherical generative model. Furthermore, the framework uses the weighted-graph model to capture all the relationships among the Twitter data efficiently. The graph is further pruned with the help of the graph component filtering approach. The graph clustering model, employed to detect the events, leverages the edge weights and the partial-k clustering approach maintaining low computation costs. The experimentation on the annotated benchmark Twitter data set and the real-world datasets show improved run-time performance up to 30% while maintaining the qualitative performance (F1-score) comparable to the state-of-the-art models.

In February 2020, COVID-19 was declared an infodemic by the experts in the situational report published by WHO. A study by the Reuters Institute for the Study of Journalism found that 40% believed that social media is the primary source of misinformation. Some research works have focused on analyzing the misinformed Twitter data using bot detection and fake-URL identification tools. However, these methods require annotated data in advance and high capacity hardware resources to deal with vast amounts of data. To overcome this challenge, we propose an unsupervised framework to detect misinformed content and the users who may be the sources of or susceptible to spreading misinformed content. This research work leverages the popularity of the tweets in form of re-posts to identify the influential content on the social media. A long w ith textual information, the credibility of the original posters of the popular tweets is assessed by using four primary credible sources i.e., News, Medical Organization, Medical Influencers, a nd Verified users. These categories are dynamically updated by using the semantic similar textual information from the user-descriptions of these user categories. The framework has been tested on two public annotated datasets from FakeNewsNet and has been compared with the baseline classification, clustering, and hybrid models for misinformation detection. Due to the unavailability of the annotated Twitter data, the resulting suspected misinformed users were searched manually on Twitter, where approximately 50% of identified users were a lready suspended. The content of misinformed clusters was validated by performing an emotional analysis of the tweets. This showed that the suspected misinformed clusters contained a high affinity towards anger and fear-inducing tweets. Furthermore, we analyzed the temporal change in geo-graphical tweet densities for all the user-categories and the popular user-interactions in the whole data.

With the growth of internet, social networks has become primary source for people to present their views on different topics. The data collected from social media are considered enough as well as reliable to be processed and gather insights on the perceptions of people towards any topic. In this research work, an empirical study of the Twitter data (i.e. around 400,000 tweets) collected for the period of December 1, 2017 to March 31, 2018, pertaining to Indian Cleanliness Campaign called Swachh Bharat Abhiyan (SBA), which focuses on improving the cleanliness situation in the country, has been done. Here, a demographic distribution of the Twitter data has been generated by augmenting partial keyword matching along with Named Entity Recognition for geoparsing the tweets. This will help to study the involvement of the people in different areas of the country. Furthermore, Sentiment Analysis of the tweets has been performed to gather the perception of people towards the campaign. Also, to assure the integrity of the campaign, the tweets have been segregated into public and government generated tweets and the respective sentiments have been compared to determine the difference in perception of public and government in different areas of the country. This work can be considered of interest because there has not been any research work which focuses on analyzing the awareness and perception of people on SBA in detail.

Frequent subgraph mining (FSM) is defined as finding all the subgraphs in a given graph that appear more number of times than a given value. It consists of two steps broadly, first is generating a candidate subgraph and second is calculating support of that subgraph. When the input to FSM algorithm is a single graph, calculating support of subgraph needs identifying its isomorphisms in the input graph. Identifying subgraph isomorphisms is NP-Complete problem. Evidently, fewer the number of candidates, fewer the support computations needed. In this paper we present a filtration technique that reduces the number of candidate subgraphs thereby reducing the overall time complexity by 7 to 18% experimentally.

Modeling data as a graph has proved an efficient approach considering huge amount of data and large number of relationships among them. The process of mining data sets, represented as graph structures, called graph mining is widely studied in bioinformatics, computer networks, chemical reactions, social networks, program flow structures, etc. Frequent Subgraph Mining is defined as finding all the subgraphs in a given graph that appear more number of times than a given value. Frequent subgraph mining process for single large graph consists of three phases, i.e., candidate generation, support computation and result generation and sets of techniques used in each phase. This paper provides a brief survey of the frequent subgraph mining algorithms focusing on the type of techniques they use in the algorithm in respective phases.