Identification and monitoring polarization from social network perspective

Abstract. Polarization is a new phenomenon that threatens the cohesion and social development of our society. The raise of social media is known to have contributed significantly to the emergence of this phenomenon as it can be noticed from the multiplication of far right and racist online communities as well as the ill-structured political discourse. This can be noticed from scrutinizing recent US or EU elections. Automatic identification of polarization from social media plays a key role in devising appropriate defence strategy to tackle the issue and avoid escalation. This thesis implements several methods to identify polarization from Twitter data issued from Trump-Clinton US election campaign using metrics like Belief Polarization Index (BPI) and Sentiment Analysis. Furtherly, semantic role labelling and argument mining were applied to derive structure of arguments of polarized discourse. Especially, we constructed thirteen topics of interests that were used as potential candidates for polarized discourse. For each topic, the cosine distance of the frequency of the topic overtime between the two candidates was used to indicate the polarization (called as Belief Polarization Index). The statistics inference of sentiment scores was implemented to convey either a positive or negative polarity, which are then further examined using argument structure. All the proposed approaches provide attempts to measure the polarization between two individuals from different perspectives, which may give some hints or references for future research.Tiivistelmä. Polarisaatio on uusi ilmiö, joka uhkaa yhteiskuntamme yhteenkuuluvuutta ja sosiaalista kehitystä. Sosiaalisen median nousun tiedetään vaikuttaneen merkittävästi tämän ilmiön syntymiseen, koska se voidaan havaita äärioikeistolaisten ja rasististen verkkoyhteisöjen lisääntymisestä sekä huonosti jäsennellystä poliittisesta keskustelusta. Tämä voidaan havaita tarkastelemalla äskettäisiä Yhdysvaltojen tai EU: n vaaleja. Polarisaation automaattisella tunnistamisella sosiaalisesta mediasta on keskeinen rooli sopivan puolustusstrategian suunnittelussa ongelman ratkaisemiseksi ja eskalaation välttämiseksi. Tässä opinnäytetyössä toteutetaan useita menetelmiä polarisaation tunnistamiseksi Yhdysvaltain Trump-Clintonin vaalikampanjan Twitter-tiedoista käyttämällä mittareita, kuten vakaumuspolarisaatio indeksi (BPI) ja mielipiteiden analyysi. Lisäksi semanttisen roolin merkintöjä ja argumenttien louhintaa sovellettiin polarisoidun diskurssin argumenttien rakenteen johtamiseen. Erityisesti rakensimme kolmetoista aihepiiriä, joita käytettiin potentiaalisina ehdokkaina polarisoituneeseen keskusteluun. Kunkin aiheen kohdalla kahden ehdokkaan aiheiden ylityötiheyden kosinietäisyyttä käytettiin osoittamaan polarisaatiota (kutsutaan nimellä Belief Polarization Index). Tunnelmapisteiden tilastollinen päättely toteutettiin joko positiivisen tai negatiivisen napaisuuden välittämiseksi, joita sitten tutkitaan edelleen argumenttirakennetta käyttäen. Kaikki ehdotetut lähestymistavat tarjoavat yrityksiä mitata kahden ihmisen välistä polarisaatiota eri näkökulmista, mikä saattaa antaa vihjeitä tai viitteitä tulevaa tutkimusta varten

Fermi Surface and Band Renormalization in (Sr,K)Fe2_2As2_2 Superconductor from Angle-Resolved Photoemission Spectroscopy

High resolution angle-resolved photoemission measurements have been carried out on (Sr,K)Fe$_2$As$_2$ superconductor (Tc=21 K). Three hole-like Fermi surface sheets are clearly resolved for the first time around the Gamma point. The overall electronic structure shows significant difference from the band structure calculations. Qualitative agreement between the measured and calculated band structure is realized by assuming a chemical potential shift of -0.2 eV. The obvious band renormalization suggests the importance of electron correlation in understanding the electronic structure of the Fe-based compounds.Comment: 4 pages, 4 figure

Distinct Fermi Surface Topology and Nodeless Superconducting Gap in (Tl0.58Rb0.42)Fe1.72Se2 Superconductor

High resolution angle-resolved photoemission measurements have been carried out to study the electronic structure and superconducting gap of the (Tl$_{0.58}$Rb$_{0.42}$)Fe$_{1.72}$Se$_2$ superconductor with a T$_c$=32 K. The Fermi surface topology consists of two electron-like Fermi surface sheets around $\Gamma$ point which is distinct from that in all other iron-based compounds reported so far. The Fermi surface around the M point shows a nearly isotropic superconducting gap of $\sim$12 meV. The large Fermi surface near the $\Gamma$ point also shows a nearly isotropic superconducting gap of $\sim$15 meV while no superconducting gap opening is clearly observed for the inner tiny Fermi surface. Our observed new Fermi surface topology and its associated superconducting gap will provide key insights and constraints in understanding superconductivity mechanism in the iron-based superconductors.Comment: 4 pages, 4 figure

Extraction of Electron Self-Energy and Gap Function in the Superconducting State of Bi_2Sr_2CaCu_2O_8 Superconductor via Laser-Based Angle-Resolved Photoemission

Super-high resolution laser-based angle-resolved photoemission measurements have been performed on a high temperature superconductor Bi_2Sr_2CaCu_2O_8. The band back-bending characteristic of the Bogoliubov-like quasiparticle dispersion is clearly revealed at low temperature in the superconducting state. This makes it possible for the first time to experimentally extract the complex electron self-energy and the complex gap function in the superconducting state. The resultant electron self-energy and gap function exhibit features at ~54 meV and ~40 meV, in addition to the superconducting gap-induced structure at lower binding energy and a broad featureless structure at higher binding energy. These information will provide key insight and constraints on the origin of electron pairing in high temperature superconductors.Comment: 4 pages, 4 figure