Unlocking the power of Twitter communities for startups

Peixoto, A. R., Almeida, A. D., António, N., Batista, F., Ribeiro, R., & Cardoso, E. (2023). Unlocking the power of Twitter communities for startups. Applied Network Science, 8, 1-21. [66]. https://doi.org/10.21203/rs.3.rs-3062630/v1, https://doi.org/10.1007/s41109-023-00593-0 --- This work was partially supported by Fundação para a Ciência e a Tecnologia, I.P. (FCT) namely by UIDB/04466/2020 and UIDP/04466/2020 (ISTAR_Iscte); UIDB/04152/2020 (MagIC/NOVA IMS); UIDB/50021/2020 (INESC-ID); and UIDB/03126/2020 (CIES_Iscte).Social media platforms offer cost-effective digital marketing opportunities to monitor the market, create user communities, and spread positive opinions. They allow companies with fewer budgets, like startups, to achieve their goals and grow. In fact, studies found that startups with active engagement on those platforms have a higher chance of succeeding and receiving funding from venture capitalists. Our study explores how startups utilize social media platforms to foster social communities. We also aim to characterize the individuals within these communities. The findings from this study underscore the importance of social media for startups. We used network analysis and visualization techniques to investigate the communities of Portuguese IT startups through their Twitter data. For that, a social digraph has been created, and its visualization shows that each startup created a community with a degree of intersecting followers and following users. We characterized those users using user node-level measures. The results indicate that users who are followed by or follow Portuguese IT startups are of these types: “Person”, “Company,” “Blog,” “Venture Capital/Investor,” “IT Event,” “Incubators/Accelerators,” “Startup,” and “University.” Furthermore, startups follow users who post high volumes of tweets and have high popularity levels, while those who follow them have low activity and are unpopular. The attained results reveal the power of Twitter communities and offer essential insights for startups to consider when building their social media strategies. Lastly, this study proposes a methodological process for social media community analysis on platforms like Twitter.publishersversionpublishe

On the modularity of 3-regular random graphs and random graphs with given degree sequences

The modularity of a graph is a parameter introduced by Newman and Girvan measuring its community structure; the higher its value (between $0$ and $1$), the more clustered a graph is. In this paper we show that the modularity of a random $3-$regular graph is at least $0.667026$ asymptotically almost surely (a.a.s.), thereby proving a conjecture of McDiarmid and Skerman stating that a random $3-$regular graph has modularity strictly larger than $\frac{2}{3}$ a.a.s. We also improve the upper bound given therein by showing that the modularity of such a graph is a.a.s. at most $0.789998$. For a uniformly chosen graph $G_n$ over a given bounded degree sequence with average degree $d(G_n)$ and with $|CC(G_n)|$ many connected components, we distinguish two regimes with respect to the existence of a giant component. In more detail, we precisely compute the second term of the modularity in the subcritical regime. In the supercritical regime, we further prove that there is $\varepsilon > 0$ depending on the degree sequence, for which the modularity is a.a.s. at least \begin{equation*} \dfrac{2\left(1 - \mu\right)}{d(G_n)}+\varepsilon, \end{equation*} where $\mu$ is the asymptotically almost sure limit of $\dfrac{|CC(G_n)|}{n}$.Comment: 41 page