Catching up with trends: The changing landscape of political discussions on twitter in 2014 and 2019

The advent of 4G increased the usage of internet in India, which took a huge number of discussions online. Online Social Networks (OSNs) are the center of these discussions. During elections, political discussions constitute a significant portion of the trending topics on these networks. Politicians and political parties catch up with these trends, and social media then becomes a part of their publicity agenda. We cannot ignore this trend in any election, be it the U.S, Germany, France, or India. Twitter is a major platform where we observe these trends. In this work, we examine the magnitude of political discussions on twitter by contrasting the platform usage on levels like gender, political party, and geography, in 2014 and 2019 Indian General Elections. In a further attempt to understand the strategies followed by political parties, we compare twitter usage by Bharatiya Janata Party (BJP) and Indian National Congress (INC) in 2019 General Elections in terms of how efficiently they make use of the platform. We specifically analyze the handles of politicians who emerged victorious. We then proceed to compare political handles held by frontmen of BJP and INC: Narendra Modi (@narendramodi) and Rahul Gandhi (@RahulGandhi) using parameters like "following", "tweeting habits", "sources used to tweet", along with text analysis of tweets. With this work, we also introduce a rich dataset covering a majority of tweets made during the election period in 2014 and 2019

First Stretch then Shrink and Bulk: A Two Phase Approach for Enumeration of Maximal (Δ,γ)(\Delta, \gamma)\mbox{-}Cliques of a Temporal Network

A \emph{Temporal Network} (also known as \emph{Link Stream} or \emph{Time-Varying Graph}) is often used to model a time-varying relationship among a group of agents. It is typically represented as a collection of triplets of the form $(u,v,t)$ that denotes the interaction between the agents $u$ and $v$ at time $t$. For analyzing the contact patterns of the agents forming a temporal network, recently the notion of classical \textit{clique} of a \textit{static graph} has been generalized as \textit{$\Delta$\mbox{-}Clique} of a Temporal Network. In the same direction, one of our previous studies introduces the notion of \textit{$(\Delta, \gamma)$\mbox{-}Clique}, which is basically a \textit{vertex set}, \textit{time interval} pair, in which every pair of the clique vertices are linked at least $\gamma$ times in every $\Delta$ duration of the time interval. In this paper, we propose a different methodology for enumerating all the maximal $(\Delta, \gamma)$\mbox{-}Cliques of a given temporal network. The proposed methodology is broadly divided into two phases. In the first phase, each temporal link is processed for constructing $(\Delta, \gamma)$\mbox{-}Clique(s) with maximum duration. In the second phase, these initial cliques are expanded by vertex addition to form the maximal cliques. From the experimentation carried out on $5$ real\mbox{-}world temporal network datasets, we observe that the proposed methodology enumerates all the maximal $(\Delta,\gamma)$\mbox{-}Cliques efficiently, particularly when the dataset is sparse. As a special case ($\gamma=1$), the proposed methodology is also able to enumerate $(\Delta,1) \equiv \Delta$\mbox{-}cliques with much less time compared to the existing methods