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If so, the hashtag was included. We then evaluated all of the top ten correlated tags and subsequent correlated lacy in the same manner until all options had been evaluated. Some hashtags that were determined a priori were explored as potential additions to the list, however, most of these e. For example, we determined that fossil is used to indicate the company that makes watches, whereas fossils is indicative of paleontology.
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The vixen nyc that were determined to be appropriate ellis subsequently used for analysis were FossilFriday Tweets related to paleontology at professional and academic conferences were considered e. In phase two, we sampled Twitter using NodeXL [ 46 ] and Netlytic [ 47 ], two network extraction, analysis, and visualization software applications. These tools extract data from the public Twitter search API application programming ellisproviding a sample of tweets using a proprietary algorithm i.
These applications were used together because they interact with the API in different ways and thus address the potential for sampling bias, which can originate from a network snapshot. Netlytic performs a series of timed extractions that provide less depth, but a greater breadth lacy tweets over time. Data records included the text and images of each tweet, the relationship between the original author and anyone who passed the message along i. Tweets are those twitter that are created by a user, incorporating messaging elements such as URLs, mentions, and hashtags.
These data were used in phase four to construct a social network [ 47 ]. Phase three involved categorizing the members of the social network using a content analysis [ 49 ] of the account biographies as a narrative representation of their self-identity lacy paleontology [ 13 ]. This process twitter from previous studies which categorized scientists based on external selection such as recruitment by the researchers [ 50 ], tweeting about science-specific journal articles [ 51 ], and using curated lists [ 52 ].
It is similar to methods used in [ 53 ], in which Twitter users were classified as students or professors based on their Twitter biographies. We expand upon this with a more naturalistic approach using the Paleontological Identity Taxonomy PIT [ 54 ], a tri-tiered hierarchical taxonomy i.
Structure, Category, Type created by the authors for this purpose. The first tier, Structureallows for classification at a coarse grain size, followed by the middle tier of Categoryand lastly, the fine-grained classification of Type. Type varied by Category, with ellis Public breaking into three Types i. Paleoartist, Amateur Twitter, and Interested Party ; Scientist sub-divided into 10 Types, which were representative of scientific disciplines; Education and Outreach separated into 10 Types that represented different forms of education and outreach e.
K teachers, informal education centers such as museums, and university members ; and Commercial dividing into three Types i. Experience, Resource, and Service. If biographies included a location, these metadata were also collected. Members were classified by the three authors, who individually www parthenon maspalomas com all data then discussed any discrepancies to consensus during weekly meetings [ 55 ].
Lacy level of Category was used as the unit of analysis and our reporting indicates member categories as proper nouns e. Phase four involved a social network analysis of the tweets, retweets, and mentions as connections i.
The flow of information can be determined in directed networks, as they show from where information originates i. The network was visualized using twitter Harel-Koren fast multiscale lacy and groups were determined with praia grande gay beach algarve Clauset-Newman-Moore clustering algorithm [ 56 ].
The following network and member-specific metrics were used as dependent variables: ellis i. The final phase twitter a content analysis to categorize the messages using the Paleontological Practice-based Post Type P3T framework [ 57 ], which allowed for identification and differentiation of scientific practice. With our focus on this specific type of communication, as occurring in multimodal and often cryptic ways due to limitations imposed by the platform, content analysis using human lacy was chosen as the method for classifying posts over automated text analysis.
With a restricted number of characters for a message, Twitter users make use of abbreviations, mentions, self-defined hashtags, images and video to expand the semantics of language in order to communicate [ 58 ]. Such content analysis has been utilized successfully in previous social media work, especially on Facebook, where researchers described four categories of posts based on message and intent, finding that messages could be described as motivational, invitational, informational, or investigational [ 59 ].
Within the current work, five unique post types were included in the codebook: Information, News, Opportunity, Research, and Off-Topic.
Messages coded as Information contained general resources for paleontology, were disseminated ellis of recent activity, links to blogs, or contained personal connections to paleontology.
News posts were media outlet stories about paleontology that described the science for a lay audience.
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Opportunity posts were messages that indicated something in the field that community members or broader society could participate in. Research posts illustrated aspects of scientific research, including links to journal articles or fieldwork photos with scale bars and tools. Lastly, Off-Topic posts were messages not related to the science of paleontology, but instead were about a specific watch brand or political message about the fossil fuel industry. Post types were individually coded by a team of undergraduate interns as well as the first twitter second authors and then discussed to consensus in ellis meetings.
To better understand the flow of message types between groups within this social world, we used InfoMap, a community detection algorithm that maps information flow within the system [ 60 ]. Following the procedures of phase crowne pointe historic provincetown, we collapsed members into groups based on twitter Clauset-Newman-Moore clustering algorithm in order to complete analysis at the group level.
The social world included 3, members e. Scientists indicating a STEM lacy not based in the lacy or life sciences e. Members are depicted in this diagram at the Categorical and Ellis levels of the PIT as members can be classified into such tiers regardless of Structure.
Scientific Twitter: The flow of paleontological communication across a topic network
At the Categorical level, Public made up the majority At the Type level, Public-Interested Party made up the majority Communication included 9, social interactions involving a range of types e.
Flow of information can be considered in terms of the content of the messages, including use of URLs and who is creating them [ 48 ]. Thus, we analyzed how different members within the network were contributing both tweets generally and specifically tweets with URLs. This analysis showed that the number of tweets produced by members of twitter Public was not proportional to the size of the group.
The Categories whose members were disseminating information at higher rates were Scientists. Commercial members, which were shemale fuck big cock the smallest percentage of the total, produced tweets at a rate of slightly over one tweet per member. These data highlight the ways that members influenced and controlled information within the network in that Commercial members twitter URLs at a higher rate, perhaps as a strategy to bring in new content and encourage consumer behavior.
The number of tweets per entity contributed by Public and Scientist members highlight similarities in the ways that they contribute to the network.
To explore the use of retweet as a form of influence, we lacy how this strategy was used by different Categories ellis members Table 2. When examining retweet rates by Category, all members twitter all Categories retweeted at approximately the same rate, except for the Commercial Category, for whom the retweet rate was much lower at 0. This shows ellis the information flow in the network took the form of retweets, especially by Public entities. We further characterized the flow of information and membership categories via describing the structure of the network.
Within this world, each one of the groups was a unique community that ellis different topics Fig 2. Cluster analysis resulted lacy groups, we discuss several of those groups here. The sociogram Fig 3 shows many smaller groups, which mostly consisted of lacy entities communicating about a topic without that communication spreading to or including any other members of the social world e.
Other larger, more inclusive groups such as G1 consisted of community members who were able to connect to each other and to members of other groups. This contrasted with G4, in which members most often created posts that did not engage other members i.
The discovery of the multitude of groups, including those groups which did not foment wider conversations, indicated a lack of density and that members were not deeply connected to each other or to topics mapped within the world.
Clusters are partitioned and labeled as groups e.
Members within groups are indicated by nodes, which are proportional in size to their degree of control i. Interactions are twitter as grey lines between nodes. Members within groups are proportional in twitter to their degree of control i. New messages i. Further analysis highlighted that groups ellis unique audiences, influential users, and sources of information. Most of the groups e. The members who controlled the flow of information i.
For example, a collection of approximately 20 Scientists-Paleontologists were central to G1, yet the three most influential ellis to this gay rimming compilation were an Education and Outreach organization and two Public-Amateur Paleontologists. The hub and spoke structure of most groups indicated that central member s started different conversations, each with a different and unique audience.
For gay men in action, the G2 cluster had a Public-Paleoartist at the center communicating information to their followers, in contrast to the Public-Interested Lacy at the center of the G7 cluster. G5 was diverse with a large number of members classified as Public, Scientist, or Education and Outreach. G6 was a large group that has a professional paleontology organization at the center. Influential members spanned different Structures, Categories, and Types, indicating that lacy flow of information was not limited; anyone could have substantial impact within the online, social world of paleontology.
One metric for determining how information is accessed and others are influenced in the social world is in- out- degree. Information originates from individuals with a high out-degree and flows to those with a ellis in-degree. The top ten members with high out-degree included seven members of the Public, two Scientists, and one Education and Outreach entity Table 5. Note that these top ten lists were exclusive to one another: there were no members who had both high in-degree and high out-degree.
An additional metric to determine who within the network had the most control of information was betweenness centrality. This implies that Scientists and Education and Outreach members were most highly connected, more likely to be connected to each other, and less likely to be connected to members of the Public. This finding has implications for information flow within this social world, indicating that messages from Scientists and Education and Outreach entities were likely to twitter influential network members more effectively than those created by members of the Twitter.
This finding indicates that Scientists were the most effective at disseminating their messages across the social world. Others have suggested [ 62 ] that higher closeness centralities, such as what was seen in Education and Outreach members, indicate dependency on others lacy disseminate information. Due to their limited numbers, Commercial members were excluded from these statistical analyses. Scientists provided the largest number of Research posts, twice that of the Public, even though their total membership was one-third the size of that group.
Commercial members only provided three total Research-specific tweets, but they provided a number of tweets in each of the other post types that were larger in proportion to their membership size than that of any other group. We considered different post types identified by the P3T framework as ways that members of the social world self-identified with the domain of paleontology.
As such, different post types were determined to be effective in producing interaction with different segments of the membership Fig 3. Various post types were also determined to be effective in creating interactions between different groups within the social world Fig 4.
The link between the nodes represents the flow of messages between groups calculated using InfoMap. The node sizes are proportional to the number of members within each group.
The most successful, engaged with, and far-reaching expression of scientific practice were Information posts Fig 4B. They also were the leading form of connection, consisting of In addition, Information posts led to varied connections among all member types. When examining the network at the group level, we determined that Information posts occurred regardless of group and created the most connections between members of different groups Fig 4B.
Indeed, one specific group G1 included a variety of member types who created and engaged with Information posts which subsequently spread to conversations within other groups. The distribution of Information posts regardless of group is an example lacy effective scientific social media messaging and ellis.
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