Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue}, author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference}, year={2013}

Eigenvalues (>1.0) and the percent variance captured by the principal components (PCs) along with the loadings.

<p>Bold print represents the most influential loadings for each eigenvector. Names assigned to each PC axis represent these influential loadings.</p><p>Eigenvalues (>1.0) and the percent variance captured by the principal components (PCs) along with the loadings.</p

Summary statistics of patch metrics for all twelve study riverine landscapes as well as summary statistics for patches broken out by the three land-use classes.

<p>Note that values for MPE and TE were scaled down by a factor of 1,000.</p><p>Summary statistics of patch metrics for all twelve study riverine landscapes as well as summary statistics for patches broken out by the three land-use classes.</p

Summary statistics of ants surveyed by riverine landscape land-use class (agriculture, mixed, developed) including total ant abundance and mean and standard deviation of density and diversity measures by patch type.

<p>Note that not all patch types were observed in all three riverine land-use classes.</p><p>Summary statistics of ants surveyed by riverine landscape land-use class (agriculture, mixed, developed) including total ant abundance and mean and standard deviation of density and diversity measures by patch type.</p

Riverine Landscape Patch Heterogeneity Drives Riparian Ant Assemblages in the Scioto River Basin, USA

<div><p>Although the principles of landscape ecology are increasingly extended to include riverine landscapes, explicit applications are few. We investigated associations between patch heterogeneity and riparian ant assemblages at 12 riverine landscapes of the Scioto River, Ohio, USA, that represent urban/developed, agricultural, and mixed (primarily forested, but also wetland, grassland/fallow, and exurban) land-use settings. Using remotely-sensed and ground-collected data, we delineated riverine landscape patch types (crop, grass/herbaceous, gravel, lawn, mudflat, open water, shrub, swamp, and woody vegetation), computed patch metrics (area, density, edge, richness, and shape), and conducted coordinated sampling of surface-active Formicidae assemblages. Ant density and species richness was lower in agricultural riverine landscapes than at mixed or developed reaches (measured using <i>S</i> [total number of species], but not using Menhinick’s Index [<i>D</i>_M]), whereas ant diversity (using the Berger-Park Index [<i>D_BP</i>]) was highest in agricultural reaches. We found no differences in ant density, richness, or diversity among internal riverine landscape patches. However, certain characteristics of patches influenced ant communities. Patch shape and density were significant predictors of richness (<i>S</i>: <i>R</i>² = 0.72; <i>D</i>_M: <i>R</i>²=0.57). Patch area, edge, and shape emerged as important predictors of <i>D_BP</i> (<i>R</i>² = 0.62) whereas patch area, edge, and density were strongly related to ant density (<i>R</i>² = 0.65). Non-metric multidimensional scaling and analysis of similarities distinguished ant assemblage composition in grass and swamp patches from crop, gravel, lawn, and shrub as well as ant assemblages in woody vegetation patches from crop, lawn, and gravel (stress = 0.18, <i>R</i>² = 0.64). These findings lend insight into the utility of landscape ecology to river science by providing evidence that spatial habitat patterns within riverine landscapes can influence assemblage characteristics of riparian arthropods.</p></div

Non-metric multidimensional scaling (NMS).

<p>NMS plots showing dissimilarity matrices of the collective relative abundance of all ant species surveyed (stress value = 0.18). Points represent class centroids (i.e., weighted means) of ant relative abundance in each patch type of each study reach (<i>n</i> = 49). The amount of variation represented by each axis is indicated in parentheses. The ellipses indicate 95% confidence intervals for clusters of each patch type and show separation in ant assemblage composition in grass/herbaceous and swamp from crop, gravel, lawn and shrub as well as woody vegetation from crop, lawn, and gravel. CR = crop, GR = grass/herbaceous, GV = gravel, LA = lawn, MU = mudflat, SH = shrub, SW = swamp, and WV = woody vegetation.</p

Location of the study system.

<p>The Scioto and Olentangy Rivers of the Scioto River basin of Ohio (USA) along with the twelve riverine landscape study reaches in agriculture, urban/developed, and mixed (forested, grassland, fallow, exurban) land-use classes.</p

Riverine landscape patch types at the twelve Scioto and Olentangy River study reaches delineated from field and remotely-sensed data.

<p>Patch classification was adapted from Johansen, Phinn and Witte [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124807#pone.0124807.ref040" target="_blank">40</a>].</p><p>Riverine landscape patch types at the twelve Scioto and Olentangy River study reaches delineated from field and remotely-sensed data.</p

Patch metrics, measures, units, and descriptions used to quantify riverine landscape composition and configuration of the twelve study reaches of the Scioto and Olentangy Rivers, Ohio, USA.

<p>Detailed metric descriptions and formulas are provided in McGarigal and Marks [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124807#pone.0124807.ref089" target="_blank">89</a>].</p><p>Patch metrics, measures, units, and descriptions used to quantify riverine landscape composition and configuration of the twelve study reaches of the Scioto and Olentangy Rivers, Ohio, USA.</p

Location of the study system comprising of part of the Hwange National Park and adjacent communal smallholder farming areas in Zimbabwe

<p>Location of the study system comprising of part of the Hwange National Park and adjacent communal smallholder farming areas in Zimbabwe</p