& Conclusion Social Network & Relationships useful for linking micro and macro levels of social ties [ ] Sociometry and quantification of social ties /
& Conclusion Social Network & Relationships useful for linking micro and macro levels of social ties [ ] Sociometry and quantification of social ties function of closeness and size ... /
& Conclusion Social Network & Relationships useful for linking micro and macro levels of social ties [ ] Sociometry and quantification of social ties function of closeness and size ... social support clique [ ] (see Fig. , based on [ ]) /
& Conclusion Social Network & Relationships useful for linking micro and macro levels of social ties [ ] Sociometry and quantification of social ties function of closeness and size ... social support clique [ ] (see Fig. , based on [ ]) /
& Conclusion ... social cohesion OSMPs – enable validation of social theories Difficult to detect cohesive groups/content veracity ... many event-typeties /
& Conclusion ... social cohesion OSMPs – enable validation of social theories Difficult to detect cohesive groups/content veracity ... many event-typeties (see Fig. ) /
& Conclusion Connections on Twitter (a) dyads (b) RT and hashtag (c) @mention (d) Relative proportion Figure: Connection types on Twitter Homophily, central to human's social interaction[ ] /
& Conclusion Connections on Twitter (a) dyads (b) RT and hashtag (c) @mention (d) Relative proportion Figure: Connection types on Twitter Homophily, central to human's social interaction[ ] ... studied at various levels – location/popularity [ ], follower–following [ ] Explore dyads or reciprocal ties ... /
& Conclusion Dataset & Model dyadic tie: a relation R over a set D is dyadic iff aRb = , ∀a, b ∈ D, see Fig. Figure: Pair of connections github.com/ijdutse/dyads_in_Twitter /
& Conclusion Dataset & Model dyadic tie: a relation R over a set D is dyadic iff aRb = , ∀a, b ∈ D, see Fig. Figure: Pair of connections queried over M accounts and retrieve dyadic ties github.com/ijdutse/dyads_in_Twitter /
& Conclusion Dataset & Model dyadic tie: a relation R over a set D is dyadic iff aRb = , ∀a, b ∈ D, see Fig. Figure: Pair of connections queried over M accounts and retrieve dyadic ties (see ) criteria: user's network G to determine reciprocal ties: G = {u|∃u ∈ G} s.t. u ∩ u = github.com/ijdutse/dyads_in_Twitter /
& Conclusion Dataset & Model dyadic tie: a relation R over a set D is dyadic iff aRb = , ∀a, b ∈ D, see Fig. Figure: Pair of connections queried over M accounts and retrieve dyadic ties (see ) criteria: user's network G to determine reciprocal ties: G = {u|∃u ∈ G} s.t. u ∩ u = Table: Many directed connections i.e. ∃a, b ∈ D, a −→ b = and b −→ a = . Category Seed Size Visited Users Retrieved Remark Unverified dyads , , , , utilised for prediction Verified dyads , , , – not used for prediction -edge and null tie , – , utilised for prediction github.com/ijdutse/dyads_in_Twitter /
& Conclusion Empirical Analysis dyads in % and % of unverified and verified profiles respectively (Fig. ) Figure: Proportion of dyads ... fewer dyads in a random collection /
& Conclusion Empirical Analysis dyads in % and % of unverified and verified profiles respectively (Fig. ) Figure: Proportion of dyads ... fewer dyads in a random collection proposed a dyad prediction model (Fig. ) /
& Conclusion Empirical Analysis dyads in % and % of unverified and verified profiles respectively (Fig. ) Figure: Proportion of dyads ... fewer dyads in a random collection proposed a dyad prediction model (Fig. ) /
& Conclusion Conclusion Porous connections on Twitter .... challenge detection of cohesive groups ... content veracity Conducted an empirical analysis to understand dyads on Twitter /
& Conclusion Conclusion Porous connections on Twitter .... challenge detection of cohesive groups ... content veracity Conducted an empirical analysis to understand dyads on Twitter Dyadsutility: improve detection tasks/content veracity prediction model /
& Conclusion Conclusion Porous connections on Twitter .... challenge detection of cohesive groups ... content veracity Conducted an empirical analysis to understand dyads on Twitter Dyadsutility: improve detection tasks/content veracity prediction model .... and large scale datasets consisting of dyadic ties /
& Conclusion Conclusion Porous connections on Twitter .... challenge detection of cohesive groups ... content veracity Conducted an empirical analysis to understand dyads on Twitter Dyadsutility: improve detection tasks/content veracity prediction model .... and large scale datasets consisting of dyadic ties future: focus on transitiveties and clustering framework based on state-typeties /
& Conclusion Reference See the full paper for further information and details about the references: Inuwa-Dutse, I., Liptrott, M. and Korkontzelos, Y., , June. Analysis and Prediction of Dyads in Twitter. InInternationalConferenceonApplicationsofNaturalLanguagetoInformation Systems (pp. - ). Springer, Cham. /