The‘‘two-stepflow’’modelofcommunication(KatzandLazarsfeld,1955;Katz,1957)statesthatmediainfluenceisexertedthroughtheintermediaryactionsof‘‘opinionleaders’’,individualswhoarehighlyengagedwithmediacontentaroundanissueandwhoacttointerpretanddisseminatenewinformationtoothers
This was in our readings this week
wefindthatmostindividualsengagedinonlinediscussionsareembeddedwithincommunitiesoflike-mindedusers;suchself-reinforcing‘‘echochambers’’canpreventengagementwithalternativeviewpointsandpromoteextremeviews
This gets back to some of our other discussions - how much does Twitter broaden our networks and how much does it limit us?
However,therewerealsoaminorityofuserswhofrequentlyinteractedwithotherswithdifferingviewsandweidentifiedcoherentmixed-attitudecommunitieswheresuchinteractionsoccurred
It would be really interesting to study these people more.
wefindthat98%,94%and68%ofusersaremembersofanechochamber,and2%,3%and28%aremembersofanopenforum,forfollower,retweetandmentionnetworks,respectively
Wow!
suggestingthatthescepticminorityhasdisproportionatelyhighvisibilityindebateusingthesehashtags
Why do you think this is?
Networkswerefilteredforvisualisation;followernetworkswerefilteredbyremovinguserswithtweetvolumebelowaspecifiedthreshold,whileretweet/mentionnetworkswerefilteredbyremovingedgeswithweightbelowaspecifiedthreshold.NetworkswerevisualisedasdirectedgraphsusingtheForceAtlas2force-directedlayoutalgorithm(Jacomyetal.,2012)providedbyGephi,suchthatcloselyconnectedusersareplacedneareachotheronatwo-dimensionalsurface
This is a good example of how to report data visualization procedures and Gephi algorithms. It is succinct and clear.
‘Follower’’networksconsistofdirectedlinksbetweenuserswho‘‘follow’’eachother,i.e.alinkA!BindicatesthatuserAisfollowedbyuserB.OnTwitter,usersreceiveallmessagestransmittedbyusersthattheyfollow.‘‘Retweet’’networksconsistofdirectedlinksindicatingthatoneuserhasre-transmittedamessagereceivedfromanother,i.e.alinkA!BindicatesthatamessageoriginallytransmittedbyuserAwasretweetedbyuserB.‘‘Mention’’networksconsistofdirectedlinksindicatingthatoneuserhasreferredtoanotheruserinoneoftheirmessages,i.e.alinkA!BindicatesthatuserAwasmentionedbyuserBinanoriginaltweet.Mentionsaresometimesusedtodrawattentionorengageinconversationwithaparticularuser.Followernetworkswereunweighted.Edgesinretweetandmentionnetworkswereweightedbynumberofoccurrencesofinteraction
Description of the networks.
Afterapreliminaryinvestigationusingakeywordsearchontheterms‘‘climatechange’’and‘‘globalwarming’’,wechosethethreemostwidelyusedhashtags(#climate,#climate-change,#globalwarming)forTwittercommunicationaboutclimatechange.Wealsochosetwohashtags(#agw,#climaterealists)thatshowedhighusagebyusersexpressingscepticorcontrarianviewsaboutclimatechange;thesewerechosentoensurerepresentationofadiversityofviewsinourdataset
I like how the researchers chose which hashtags to include in order to chose the most relevant ones from both sides of the debate. This makes the data more comprehensive.
Ourstudyaimedtocharacterisesocialmediadiscussionsofclimatechangebymappingthestructureofusersocialnetworks,measuringthedistributionofuserattitudesacrossthosenetworks,andexploringuserinteractionsandbehaviours
more information on the purpose and methodology
Forexample,Postmesetal.(2000)examinedsocialnormsinstudentemailcommu-nicationsaroundaweb-delivereduniversitycourse,findingthatnormswereestablishedbyaniterativeprocessofobservationandactive(re)negotiation,andthattheyplayedanimportantroleindefiningnewlyemergentsocialgroups.
This finding is really interesting. It certainly makes sense that iterative processes of negotiating group norms would work well in forums with a written history of interactions (like email or Twitter). Newcomers have easy access to the history of interactions
Thedecentralisedandparticipatorynatureofonlinesocialmediaoffersanovelopportunitytostudypreviouslyinaccessibleaspectsofsocialinteractionaboutclimatechange(Aueretal.,2014),includingthesocialnetworkstructuresthatlinkindividualsengagedinonlinedebateandthatarelikelytoaffecthowattitudesevolveovertime
The purpose of the study
What do the structural properties suggest about theorganization of the hate groups? As mentioned in point(a), the structure of the network suggests that the hategroups in blogosphere have not formed into centralizedorganizations
I wonder if this same finding would be true if the study was replicated using Twitter in the current context of the U.S. On the one hand, Twitter makes it easier for individuals to communicate ideas and connect to each other, and yet there are also sites like Brietbart that push out a great deal of information. I wonder if there has been a shift toward more centralization because of that?
The largest connected component, often called agiant component in graph theory (Bolloba ́s, 1985),contained 273 nodes connected by 1115 links. This giantcomponent was a rather dense graph with an average nodedegree of 8.2
Information on density of the largest connected component
In arandom network the probability that two randomlyselected nodes are connected is a constantp. As a result,each node has roughly the same number of links and nodesare rather homogenous. In addition, communities are notlikely to exist in random networks. Small-world networks,in contrast, have a significantly high tendency to formgroups and communities. Most empirical networks rangingfrom social networks (Newman, 2004a), biological net-works (Jeong et al., 2001), to the Web (Albert et al., 1999)have been found to be nonrandom networks. In addition,many of these networks are also scale-free networks(Baraba ́si et al., 1999), in which a large percentage ofnodes have just a few links, while a small percentage of thenodes have a large number of links. Thus, nodes in scale-free networks are not homogenous in terms of their links.Some nodes become hubs or leaders that play importantroles in the operation of the network. The Web has beenfound to have both small-world and scale-free properties(Albert and Baraba ́si, 2002).
This is a really helpful description of these types of networks. I know we've read this information before, but somehow this description resonates with me more than others
For example, the central nodes often play a key role byissuing commands or bridging different communities. Theremoval of central nodes can effectively disrupt a networkthan peripheral nodes
Ways SNA can give good information on dynamics of online networks
However, it does suggest the possibility that some donations to research-oriented institutions support the research enterprise more than undergraduate education. By contrast, donations to teaching-oriented institutions may more directly influence under-graduate education.
This would be a really interesting topic for a future study. Could you use SNA to track money (or general social capital) throughout an institutional network? For example, I would imagine that a great deal of foundation funding does go toward research and not direct instruction or undergraduate education resources. And yet, that probably does have some direct and indirect positive influences on the educational initiatives at the institution.
Whereas 10–20% of undergraduate students receive Pell grants at the institutions with the highest eigenvector centrality, the same is true of 50–80% of students at the institutions with the lowest eigenvector centrality.
This certainly points to how inequities are reproduced through education
hus, Rogers (2015) concluded that foundations are now involved in a multitude of ways in education policy reform: identifying problems, funding research, creating solu-tions, selecting and training the individuals implementing policy, influencing government appointments, funding start-up programs, and financing selective media coverage.
This seems a lot like the false public sphere. The vertical level control means that many of the ideas are actually coming from the same entities, as opposed to various and diverse entities. This constricts not only participation in the public sphere, but also the content in the public sphere.
This critique, now over thirty years old, has not substantially altered the nature of the foundation-institution relation-ship in higher education
related to Giny's post above - the could have supported their argument better if the provided additional, more recent articles and viewpoints.
Another significant finding is that efforts of the membersof religious networks—in spite of their relatively closedcharacteristics—in terms of being at the center of a net-work and taking the brokerage role are, contrary to theliterature, highly developed
This is an important finding that can help researchers better understand how this and similar religious networks operate.
congregational network members take abrokerage role much more often than the secular; accord-ingly, they are more active in providing relations and thetransfer of information between networks
This is really interesting - and goes along with the hypotheses
sity rate ofnetwork (%)0.773
This also seems pretty dense
Density rate ofnetwork (%)0.88
What we learned about this week - that seems like a pretty dense network
While previousstudies presented that social networks have an influence onreligiousness, they did not respond to the question of howreligious ties structure attitudes and relations in the orga-nizational field
What this study adds to the existing literature
We deleted non-directed tweets, but itshould be acknowledged that non-directed tweets may also bear implications for knowledgesharing and can be examined in future studies. The data cleaning procedure also excludedretweets and tweets that serve as quoting.
This is a detailed description of their data cleaning methodology. It is good to know to help understand the results of this research study, but is also helpful for me to understand how to clean such large data sets.
The second goal of SNA is to identify theflow of information across various types of actors. Knowledge sharing involves participantsfrom a diverse set of professional backgrounds, with varying degrees of healthcare experience,expertise and expectation (Stewart and Abidi 2012)
node attributes
Nevertheless, they allcarry out a common practice—that is contributing health information that potentially impactsthe knowledge flow
This is a really good point - the reasons people use hashtags can be different for different topics. Whereas the example of wikileaks may have been a more diffuse and ad hoc conversation, it makes sense that people would use health related hashtags in a more community based way. It is the difference between tweeting about a current event versus tweeting about an ongoing topic. Definitely a good reason to study this more.
Twitter also facilitates collective actions such as organizingweekly Q&A sessions. For example,#AlzChatis for a live tweet chat that takes place everyMonday on the topic of Alzheimer’s disease. The conversations and joint activities form thecommunity component in CoPs.
Yes - the weekly chat usage of Twitter is a perfect example of how people use defined and well-known hashtags to create and participate in CoPs
Still, it may not be others' actual behavior that drives ourown ad-dictive behavior, but our perceptions of their behavior, where the twoconflict.
This relates to the above annotation. I also agree that people's perceptions of others behaviors are unreliable; but this is an interesting point that in this case, perception may be more important than reality. Of course, that would need to be tested for us to know for sure. I think an interesting future study would be to use SNA and have both the egos and their alters actually track their substance use activities day by day. This would address the perception vs reality issue, as well as the underestimation of own behavior issue. There could still be some social desirability bias though.
Outsiders in-volved in resource management or disaster mitigation must becognizant of the these kinds of underlying dynamics of knowl-edge generation in indigenous communities and how knowl-edge may put be put into practice by individuals who are at theintersection of local and global knowledge.
This shows the power of SNA in this type of research - the key players and the way information flows through the network is different from what had been assumed. Instead of assuming who the important actors are, SNA allows researchers and practitioners to see the actual patterns and adjust their approach from there.
It is important to note that the bivariate screening forthe reduced sample (N=32) found that none of the socialnetwork variables had a statistically significant relation-ship with the dependent variable. We acknowledge, how-ever, that time constraints prevented the collection of so-cial network data from every individual and, therefore, amore thorough analysis of the global pattern of connec-tions between nodes. Of the two measures of centralitythat were calculated, we have much more confidence inthe degree measurement than the betweenness measure-ment because degree is not affected by an incompletenetwork sample as it measures direct interaction betweentwo individuals. Our betweeness measurement, on theother hand, has limited explanatory power when calculat-ed for an incomplete network and should be viewed asonly a rough guideline.
This relates to Ginny's earlier question about the reliability of results given the small sample size.
wo degree and two betweenness cen-trality measures
Degree and betweenness centrality measures
We also asked people to indicate whom theywould turn to find out something they did not understandabout the marine environment. We determined this to be theirexpert network.
Not knowing much about this research area, it seems important that the researchers delineated between social network and expert network. It seems like both would be important for this topic.
To generateour subsample for the social network study, we employed asnowball sampling method where we initially asked 17 re-spondents about their social and expert networks. This in-volved asking informants to name the seven most importantpersons in their lives, starting with the most important, outsideoftheir household
This is a very clear description of the SNA aspect of the study - both the sampling methodology and the questions.
As with most team cohe-sion research, socially desirable participant responses may be a limitation.
I wonder if there is a role for observation in this that might counter some of the social desirabilty bias.
This means that the focus of academic capitalism (as atheory) is not only restricted to commercialisation of research, but also takesinto consideration other aspects of universities (e.g. instruction and adminis-tration) and changing relations within universities (e.g. decreasing status ofmany social sciences and humanities), as well as between universities and theirsocial environment.
This passage made me think about the non-academic units involved in this expanded view of academic capitalism. It would be really interesting to map the network of departments within an institution to see what role they play in academic capitalism - especially those not specifically assigned with knowledge production. I'm thinking of admissions offices, financial aid, government relations, and similar departments.
interstitial organisations that facilitate collaboration between universitiesand private companies;intermediating organisations and networks between public and privatesector;
these interstitial and intermediating organizations form networks of their own - and in many respects exist primarily because of academic capitalism. Some have very strong ties to academic units and institutions because they are dependent on those. These organizations can also serve as brokers by connecting the institutional nodes to nodes located in business or the community.