![]() We suggest that the study of automated agents should include designer intentions, the design and behavior of automated agents, user expectations, as well as unintended and incidental effects of interaction. The analysis demonstrated that the bot dominated the network in terms of engagement (out-degree) and the ability to connect distant clusters of actors (betweenness centrality) while more traditional actors, such as the main election candidates and news accounts, indicated more prestige (in-degree) and power (eigenvector centrality). However, the co-use of #Sri Lanka and/or #lk with #PresPollSL, a hashtag used to discuss politics related to Sri Lanka’s presidential election in 2015, resulted in the bot incidentally amplifying the political voice of less engaged actors. ![]() The bot served the simple function of retweeting tweets with hashtags #SriLanka and #lk to its follower network. We examined a Twitter network data set with 1,782 nodes and 5,640 edges to demonstrate the engagement and outreach of a retweeting bot called Siripalabot that was popular among Sri Lankan Twitter users. practices can result in incidental effects from automated agents. We suggest that the interplay between social media affordances and user. The current body of research discusses how bots can be designed to achieve specific purposes as well as instances of unexpected negative outcomes of such use. The role of automated or semiautomated social media accounts, commonly known as “bots,” in social and political processes has gained significant scholarly attention. As we will show, the approach can be easily transferred to other networks. However, these do not aim to reduce complexity, what is a necessary requirement to be applicable to real-world online social networks. There are already profile matching approaches based on timing patterns. In contrast to most existing approaches that rely on user definable attributes, we rather focus on timing properties of user publications across social media platforms. Furthermore, profile attributes and relationships are not trustworthy, as these are due to arbitrary change by profile owners. Additionally to the complexity problem of existing approaches, many profiles with similar attributes often lead to a restrictive trade-off between precision and recall of the matching strategy. We provide a proof of concept by an implementation of the use case of matching user profiles accross Twitter and Instagram. Therefore, we present an approach to significantly reduce complexity by exploiting special properties of dataset IDs. Complex approaches are not well suited to handle large datasets. These approaches require high effort concerning computation, because each profile of one network has to be compared to all profiles of the other network. Almost all of them rely on common profile attributes like names and hobbies or structural attributes like relations to other user profiles. You have to install it first.Many publications deal with profile matching across online social networks and the approaches become increasingly complex. No can do with Tweetdeck, because it’s not Web-based. What if I’m at my sister’s place, watching the softball NCAA championships, and I get an email from a client that requires me to make a change to tweet I scheduled earlier that day? With Hootsuite, I just jump onto my sister’s computer, pull up the site and log in, and make the change. Then there’s the runner-up deal-breaker: I can’t access Tweetdeck from anywhere. They make it easier to see what I want to see, and help me avoid tweeting from my work account when I’m trying to tweet from - which, of course, can be a career-ruining mistake. But if you have a handle for yourself and for work - and clients on top of that - one screen with a zillion columns doesn’t cut it. Which is fine if you’re just using the tool for your own personal Twitter handle. Here’s the deal-breaker for Tweetdeck: It doesn’t offer tabs for different accounts. Pretty please? UPDATE: The Pending Tweets column is back! Score for Hootsuite! But after just two days, I’m going back to Hootsuite - and hoping they re-implement the Pending Tweets column. I downloaded it both on my work computer and at home, and went about making it look the way I like. It usually works great, but I tend to remember the mornings when it takes forever to process, because those are the days when I scramble to get to my day job on time. Because I schedule tweets in bulk for clients every morning, I need to be able to easily see pending tweets without clicking over to another page. Except that when Hootsuite implemented it, they deleted the Pending Tweets column on the main dashboard, causing an inconvenience for users like me. The company added a publisher function that allows teams of users to better coordinate. But two weeks ago Hootsuite made a change that has its community up in arms.
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